Generalized extreme learning machine autoencoder and a new deep neural network

Sun, Kai; Zhang, Jiangshe; Zhang, Chunxia; Hu, Junying

doi:10.1016/j.neucom.2016.12.027

Cited by 154Machine Learning and ELMNeural Networks and ApplicationsFace and Expression Recognition

Generalized extreme learning machine autoencoder and a new deep neural network

Sun, Kai, Zhang, Jiangshe, Zhang, Chunxia, Hu, Junying·2017·Neurocomputing

DOI 10.1016/j.neucom.2016.12.027

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.neucom.2016.12.027

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Autoencoders can be used as a preprocessing step before a classical classifier or as components within a deep CNN.
“Since they can remove noise and simplify data, AEs can be used as a preprocessing step before a classical classifier (such as a SVM) [75] or even as components within a deep CNN [76,77].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [76]

Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285 · see citing works
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,519 · see citing works
On Spectral Clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001) · Cited by 8,159 · see citing works
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562 · see citing works
Combinatorial Optimization: Algorithms and Complexity.
Johnson, D. S., Papadimitriou, C. H., & Steiglitz, K. (1984) · American Mathematical Monthly · doi:10.2307/2322374 · Cited by 6,007 · see citing works
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
Extreme learning machine: a new learning scheme of feedforward neural networks
Huang, G.-B., Zhu, Q.-Y., & Siew, C. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1380068 · Cited by 4,248 · see citing works
Untitled work
(2006) · Cited by 3,624 · see citing works
Combinatorial optimization:Algorithms and complexity
Rabiner, L. R. (1984) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1984.1164450 · Cited by 3,590 · see citing works
Extensions of Lipschitz mappings into a Hilbert space
Johnson, W. B., & Lindenstrauss, J. (1984) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/026/737400 · Cited by 2,632 · see citing works
Acquiring linear subspaces for face recognition under variable lighting
Lee, K.-C., Ho, J., & Kriegman, D. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.92 · Cited by 2,435 · see citing works
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
BelkinMikhail, NiyogiPartha, & SindhwaniVikas (2006) · Journal of Machine Learning Research · doi:10.5555/1248547.1248632 · Cited by 2,129 · see citing works
Extreme Learning Machines [Trends & Controversies]
Cambria, E., Huang, G.-B., Kasun, L. L. C., Zhou, H., Vong, C., Lin, J., Yin, J., Cai, Z., Liu, Q., Li, K., et al. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.140 · Cited by 710 · see citing works
Extensions of Lipschitz mappings into a Hilbert space
Johnson, W. B. (1984) · Medical Entomology and Zoology · Cited by 661 · see citing works
Combinatorial optimization: algorithms and complexity
Papadimitriou, C. H., & Steiglitz, K. (1998) · CERN Document Server (European Organization for Nuclear Research) · Cited by 486 · see citing works
Beyond the point cloud
Sindhwani, V., Niyogi, P., & Belkin, M. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102455 · Cited by 445 · see citing works
Human face recognition based on multidimensional PCA and extreme learning machine
Mohammed, A. A., Minhas, R., Wu, Q. M. J., & Sid-Ahmed, M. A. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.013 · Cited by 321 · see citing works
Laplacian Support Vector Machines Trained in the PrimalPreprint
Melacci, S., & Belkin, M. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0909.5422 · Cited by 315 · see citing works
Learning Algorithms for the Classification Restricted Boltzmann Machine
Larochelle, H., Sherbrooke, U. D., Mandel, M., Pascanu, R., Bengio, Y., & Lee, D. (2012) · Cited by 277 · see citing works
Laplacian Support Vector Machines Trained in the Primal
MelacciStefano, & BelkinMikhail (2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2021038 · Cited by 217 · see citing works
Deep extreme learning machines: supervised autoencoding architecture for classification
Tissera, M. D., & McDonnell, M. D. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.110 · Cited by 131 · see citing works
Learning deep representations via extreme learning machines
Yu, W., Zhuang, F., He, Q., & Shi, Z. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.03.077 · Cited by 104 · see citing works
Clustering in extreme learning machine feature space
He, Q., Jin, X., Du, C., Zhuang, F., & Shi, Z. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.12.063 · Cited by 97 · see citing works
Using manifold structure for partially labelled classification
Belkin, M., & Niyogi, P. (2002) · neural information processing systems · Cited by 88 · see citing works
Leukocyte image segmentation by visual attention and extreme learning machine
Pan, C., Park, D. S., Yang, Y., & Yoo, H. M. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0522-9 · Cited by 85 · see citing works
Using Manifold Stucture for Partially Labeled Classification
Belkin, M., & Niyogi, P. (2002) · neural information processing systems · Cited by 64 · see citing works
A new deep neural network based on a stack of single-hidden-layer feedforward neural networks with randomly fixed hidden neurons
Hu, J., Zhang, J., Zhang, C., & Wang, J. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.06.017 · Cited by 30 · see citing works
Self-citation

Flagged first, then by citations

1 of the 33 references couldn’t be matched to a known paper.

Sorted by citations

A review on extreme learning machine
Wang, J., Lu, S., Wang, S.-H., & Zhang, Y.-D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11007-7 · Cited by 572 · see citing works
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020) · Materials · doi:10.3390/ma13245755 · Cited by 515 · see citing works
A review on neural networks with random weights
Cao, W., Wang, X., Ming, Z., & Gao, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.040 · Cited by 472 · see citing works
Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network
Khamparia, A., Gupta, D. K., Nguyen, G. N., Khanna, A. K., Pandey, B., & Tiwari, P. (2019) · IEEE Access · doi:10.1109/access.2018.2888882 · Cited by 238 · see citing works
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
Zhang, J., Li, Y., Xiao, W., & Zhang, Z. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.04.033 · Cited by 179 · see citing works
Self-citation
Deep neural networks with Elastic Rectified Linear Units for object recognition
Jiang, X., Pang, Y., Li, X., Pan, J. S., & Xie, Y. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.056 · Cited by 138 · see citing works
Stacked autoencoder based deep random vector functional link neural network for classification
Katuwal, R., & Suganthan, P. N. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105854 · Cited by 117 · see citing works
A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works
“Since they can remove noise and simplify data, AEs can be used as a preprocessing step before a classical classifier (such as a SVM) [75] or even as components within a deep CNN [76,77].”
Method
Noise robust sound event classification with convolutional neural network
Özer, İ., Özer, Z., & Fındık, O. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.021 · Cited by 93 · see citing works
A survey on metaheuristic optimization for random single-hidden layer feedforward neural network
Han, F., Jiang, J., Ling, Q.-H., & Su, B. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.080 · Cited by 90 · see citing works
Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images
Pattanaik, P. A., Mittal, M., & Khan, M. Z. (2020) · IEEE Access · doi:10.1109/access.2020.2996022 · Cited by 78 · see citing works
Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis
Shi, M., Ding, C., Wang, R., Song, Q., Shen, C., Huang, W., & Zhu, Z. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110172 · Cited by 74 · see citing works
Broad Learning Based Dynamic Fuzzy Inference System With Adaptive Structure and Interpretable Fuzzy Rules
Bai, K., Zhu, X., Wen, S., Zhang, R., & Zhang, W. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3112222 · Cited by 66 · see citing works
Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing
Zhao, X., Jia, M., Bin, J., Wang, T., & Liu, Z. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3041087 · Cited by 57 · see citing works
Improving variational autoencoder with deep feature consistent and generative adversarial training
Hou, X., Sun, K., Shen, L., & Qiu, G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.013 · Cited by 60 · see citing works
Self-citation
Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning
Kshirsagar, G. B., & Londhe, N. D. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2875024 · Cited by 57 · see citing works
A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing
Karim, A., Güzel, M. S., Tolun, M. R., Kaya, H., & Çelebi, F. V. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/3145947 · Cited by 53 · see citing works
Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines
Shao, W., Ge, Z., Song, Z., & Wang, K. (2019) · Control Engineering Practice · doi:10.1016/j.conengprac.2019.07.016 · Cited by 53 · see citing works
Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification
Ahmad, M. I., Shabbir, S., Oliva, D. A., Mazzara, M., & Distefano, S. (2020) · Optik · doi:10.1016/j.ijleo.2019.163712 · Cited by 45 · see citing works
Investigating the use of motion-based features from optical flow for gait recognition
Mahfouf, Z., Merouani, H. F., Bouchrika, I., & Harrati, N. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.12.040 · Cited by 43 · see citing works
Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm
Dash, P. K., Prasad, E. N. V. D. V., Jalli, R. K., & Mishra, S. P. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.118454 · Cited by 42 · see citing works
Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder
Fan, Z., Du-yan, B., He, L., Ma, S., Gao, S., & Li, C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.066 · Cited by 40 · see citing works
Restricted Boltzmann machine to determine the input weights for extreme learning machines
Pacheco, A. G. C., Krohling, R. A., & da Silva, C. B. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.11.054 · Cited by 39 · see citing works
Coal classification method based on visible-infrared spectroscopy and an improved multilayer extreme learning machine
Mao, Y., Le, B. T., Xiao, D., He, D., Liu, C., Jiang, L., Yu, Z., Yang, F., & Liu, X. (2019) · Optics & Laser Technology · doi:10.1016/j.optlastec.2019.01.005 · Cited by 38 · see citing works
Multilayer probability extreme learning machine for device-free localization
Zhang, J., Xiao, W., Li, Y., Zhang, S., & Zhang, Z. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.106 · Cited by 37 · see citing works
Self-citation
Learning local discriminative representations via extreme learning machine for machine fault diagnosis
Li, Y., Zeng, Y., Qing, Y., & Huang, G.-B. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.021 · Cited by 37 · see citing works
A Review of multilayer extreme learning machine neural networks
Vásquez-Coronel, J. A., Mora, M., & Vilches-Ponce, K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10478-4 · Cited by 39 · see citing works
A Combination Interval Prediction Model Based on Biased Convex Cost Function and Auto-Encoder in Solar Power Prediction
Long, H., Zhang, C., Geng, R., Wu, Z., & Gu, W. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2021.3054125 · Cited by 36 · see citing works
Self-citation
A joint residual network with paired ReLUs activation for image super-resolution
Tang, Z., Luo, L., Peng, H., & Li, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.061 · Cited by 35 · see citing works
A comprehensive review of extreme learning machine on medical imaging
Huérfano, Y., Mora, M., Vilches-Ponce, K., Hernández-García, R., Gutiérrez, R., & Vera, M. Á. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126618 · Cited by 34 · see citing works
A distributed overlapping community detection model for large graphs using autoencoder
Bhatia, V., & Rani, R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.045 · Cited by 34 · see citing works
A hybrid system for forecasting 24-h power load profile for Polish electric grid
Brodowski, S., Bielecki, A., & Filocha, M. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.04.053 · Cited by 34 · see citing works
Marginal Stacked Autoencoder With Adaptively-Spatial Regularization for Hyperspectral Image Classification
Feng, J., Liu, L., Cao, X., Jiao, L., Sun, T., & Zhang, X. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2854893 · Cited by 33 · see citing works
A two-channel convolutional neural network for image super-resolution
Li, S., Fan, R., Lei, G., Yue, G., & Hou, C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.041 · Cited by 32 · see citing works
Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time‐Frequency Representation of PCG Recordings
Ghosh, S. K., Ponnalagu, R. N., Tripathy, R. K., & Acharya, U. R. (2020) · BioMed Research International · doi:10.1155/2020/8843963 · Cited by 32 · see citing works
Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary
Waheeb, S. A., Khan, N. A., Chen, B., & Shang, X. (2020) · Information · doi:10.3390/info11050281 · Cited by 30 · see citing works
Hierarchical One-Class Classifier With Within-Class Scatter-Based Autoencoders
Wang, T., Cao, J., Lai, X., & Wu, Q. M. J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015860 · Cited by 30 · see citing works
Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing
Shi, M., Ding, C., Que, H., Wu, C., Shi, J., Shen, C., Huang, W., & Zhu, Z. (2022) · Measurement · doi:10.1016/j.measurement.2022.112299 · Cited by 30 · see citing works
Broad Learning Autoencoder With Graph Structure for Data Clustering
Yu, Z., Zhong, Z., Yang, K., Cao, W., & Chen, C. L. P. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3283425 · Cited by 28 · see citing works
Graph Embedding-Based Dimension Reduction With Extreme Learning Machine
Yang, L., Song, S., Li, S., Chen, Y., & Huang, G. M. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2931003 · Cited by 30 · see citing works
Minimum Variance Embedded Random Vector Functional Link Network
Ganaie, M. A., Tanveer, M., & Suganthan, P. N. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-63823-8_48 · Cited by 29 · see citing works
Weighted Gate Layer Autoencoders
El-Fiqi, H., Wang, M., Kasmarik, K., Bezerianos, A., Tan, K. C., & Abbass, H. A. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3049583 · Cited by 27 · see citing works
Coal Quality Exploration Technology Based on an Incremental Multilayer Extreme Learning Machine and Remote Sensing Images
Le, B. T., Xiao, D., Mao, Y., He, D., Xu, J., & Song, L. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2890040 · Cited by 27 · see citing works
Variances-constrained weighted extreme learning machine for imbalanced classification
Liu, Z., Jin, W., & Mu, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.052 · Cited by 27 · see citing works
A novel recurrent hybrid network for feature fusion in action recognition
Yu, S., Cheng, Y., Xie, L., Luo, Z., Huang, M., & Li, S. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.09.007 · Cited by 26 · see citing works
Data multiplexed and hardware reused architecture for deep neural network accelerator
Raut, G., Biasizzo, A., Dhakad, N. S., Gupta, N., Papa, G., & Vishvakarma, S. K. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.018 · Cited by 24 · see citing works
Monocular depth estimation with guidance of surface normal map
Han, Y., Zhang, S., Zhang, Y., & Zhang, L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.074 · Cited by 24 · see citing works
Feature learning for stacked ELM via low-rank matrix factorization
Ouyang, T. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.110 · Cited by 24 · see citing works
Runoff Prediction and Analysis Based on Improved CEEMDAN-OS-QR-ELM
Liu, Y., Li-hu, W., Yang, L., Liu, X., & Wang, L. (2021) · IEEE Access · doi:10.1109/access.2021.3072673 · Cited by 24 · see citing works
Extreme deep learning in biosecurity: the case of machine hearing for marine species identification
Demertzis, K., Iliadis, L. S., & Anezakis, V.-D. (2018) · Journal of Information and Telecommunication · doi:10.1080/24751839.2018.1501542 · Cited by 24 · see citing works
Diagnosis of unbalanced rolling bearing fault sample based on adaptive sparse contrative Auto-encoder and IGWO-USELM
Song, H., Ma, W., Han, Z., & Zhu, X. (2022) · Measurement · doi:10.1016/j.measurement.2022.111353 · Cited by 23 · see citing works
Review of Segmentation Methods for Coastline Detection in SAR Images
Ciecholewski, M. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-10000-7 · Cited by 26 · see citing works
FPCANet: Fisher discrimination for Principal Component Analysis Network
Sun, K., Zhang, J., Yong, H., & Liu, J. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.12.015 · Cited by 23 · see citing works
Self-citation
Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction
Adegoke, M., Alaka, H., Ajayi, S. O., & Olu-Ajayi, R. (2022) · Energies · doi:10.3390/en15249512 · Cited by 22 · see citing works
Graph-based broad learning system for classification
Liu, Z., Huang, S., Jin, W., & Mu, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.065 · Cited by 21 · see citing works
Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation
Ge, H., Sun, W., Zhao, M., & Yao, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2894014 · Cited by 20 · see citing works
Fault diagnosis of rotating machinery components using a deep kernel extreme learning machine under different working conditions
Du, G., Xu, Q., & Yang, X. (2020) · Measurement Science and Technology · doi:10.1088/1361-6501/ab9037 · Cited by 20 · see citing works
Financial Time Series Forecasting Using Deep Learning Network
Preeti, P., Dagar, A., Bala, R., & Singh, R. P. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-2035-4_3 · Cited by 19 · see citing works
An effective hierarchical extreme learning machine based multimodal fusion framework
Du, F., Zhang, J., Ji, N., Shi, G., & Zhang, C. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.005 · Cited by 18 · see citing works
Self-citation
Prediction of apoptosis protein subcellular localization via heterogeneous features and hierarchical extreme learning machine
Zhang, S., Zhang, T., & Liu, C. (2019) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2019.1576222 · Cited by 18 · see citing works
Hierarchical extreme learning machine with L21-norm loss and regularization
Li, R., Wang, X., Song, Y., & Lei, L. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01234-z · Cited by 18 · see citing works
A Dual Stream Spectrum Deconvolution Neural Network
Deng, L., Xu, G., Dai, Y., & Zhu, H. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3106971 · Cited by 17 · see citing works
Many-objective coevolutionary learning algorithm with extreme learning machine auto-encoder for ensemble classifier of feedforward neural networks
Li, H., Bai, L., Gao, W., Xie, J. H., & Huang, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123186 · Cited by 17 · see citing works
Deep learning based image Super-resolution for nonlinear lens distortions
Chang, Q., Hung, K.-W., & Jiang, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.035 · Cited by 17 · see citing works
An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics
Zhang, C., Peng, T., Zhou, J., Ji, J., & Wang, X. (2019) · Applied Sciences · doi:10.3390/app9193987 · Cited by 16 · see citing works
Self-citation
Super-resolution using multi-channel merged convolutional network
Chu, J., Li, X., Zhang, J., & Lu, W. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.04.089 · Cited by 15 · see citing works
Self-citation
Ensemble deep random vector functional link for self-supervised direction-of-arrival estimation
He, J., Li, X., Liu, P., Wang, L., Zhou, H., Wang, J., & Tang, R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105831 · Cited by 14 · see citing works
A review and empirical analysis of neural networks based exchange rate prediction
Pandey, T. N., Jagadev, A. K., Dehuri, S., & Cho, S.-B. (2018) · Intelligent Decision Technologies · doi:10.3233/idt-180346 · Cited by 14 · see citing works
Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks
Shaohui, D., Chen, Z., Wu, H., Tang, Y., & Li, Y. (2021) · Complexity · doi:10.1155/2021/5196190 · Cited by 14 · see citing works
Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network
Tang, X., Zhang, P., Du, J. Q., & Xu, Z. (2021) · Spectroscopy Letters · doi:10.1080/00387010.2021.1982988 · Cited by 14 · see citing works
Explaining classifiers by constructing familiar concepts
Schneider, J., & Vlachos, M. (2022) · Machine Learning · doi:10.1007/s10994-022-06157-0 · Cited by 14 · see citing works
DS-P3SNet: An Efficient Classification Approach for Devanagari Script-Based P300 Speller Using Compact Channelwise Convolution and Knowledge Distillation
Kshirsagar, G. B., & Londhe, N. D. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3156861 · Cited by 14 · see citing works
Least-Squares Fuzzy Transforms and Autoencoders: Some Remarks and Application
Tomasiello, S. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.3007442 · Cited by 14 · see citing works
Supervised Extreme Learning Machine-Based Auto-Encoder for Discriminative Feature Learning
Du, J., Vong, C., Chen, C., Liu, P., & Liu, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2962067 · Cited by 13 · see citing works
Aircraft Type Recognition in Remote Sensing Images: Bilinear Discriminative Extreme Learning Machine Framework
Zhao, B., Tang, W., Pan, Y. R., Han, Y., & Wang, W. (2021) · Electronics · doi:10.3390/electronics10172046 · Cited by 13 · see citing works
A 3D Object Recognition Method From LiDAR Point Cloud Based on USAE-BLS
Tian, Y., Song, W., Chen, L., Fong, S. J., Sung, Y., & Kwak, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3140112 · Cited by 15 · see citing works
Neighborhood graph embedding interpretable fault diagnosis network based on local and non-local information balanced under imbalanced samples
Sun, Z., Wang, Y., Sun, G., Gao, J., & Pan, Z. (2022) · Structural Health Monitoring · doi:10.1177/14759217221112835 · Cited by 12 · see citing works
Locality Preserving Projections-Based Spatiotemporal Modeling of the Temperature Distribution of Lithium-Ion Batteries
Zhu, C., Yang, H., Jin, X., Xu, K., & Shen, W. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3257311 · Cited by 11 · see citing works
Novel single and multi-layer echo-state recurrent autoencoders for representation learning
Chouikhi, N., Ammar, B., Hussain, A., & Alimi, A. M. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105051 · Cited by 11 · see citing works
A two-stream abnormal detection using a cascade of extreme learning machines and stacked auto encoder
Gnouma, M., Ejbali, R., & Zaied, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15060-2 · Cited by 11 · see citing works
A method for diagnosing rolling bearing faults based on SDAE-ADHKELM
Li, X., & Jin, W. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac9709 · Cited by 10 · see citing works
Clustering Based on Supervised Learning of Exemplar Discriminative Information
Duan, L., Cui, S., Qiao, Y., & Yuan, B. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2870549 · Cited by 10 · see citing works
Gate-Layer Autoencoders with Application to Incomplete EEG Signal Recovery
El-Fiqi, H., Kasmarik, K., Bezerianos, A., Tan, K. C., & Abbass, H. A. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852101 · Cited by 10 · see citing works
Representation learning by hierarchical ELM auto‐encoder with double random hidden layers
Li, R., Wang, X., Lei, L., & Wu, C. (2019) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5590 · Cited by 10 · see citing works
Deep Learning of Fuzzy Weighted Multi-Resolution Depth Motion Maps with Spatial Feature Fusion for Action Recognition
Al-Faris, M., Chiverton, J. P., Yang, Y., & Ndzi, D. L. (2019) · Journal of Imaging · doi:10.3390/jimaging5100082 · Cited by 9 · see citing works
WITHDRAWN: Residual Dense Convolutional Neural Network for Image Super-Resolution
Abbass, M. Y. (2020) · Optik · doi:10.1016/j.ijleo.2020.165341 · Cited by 9 · see citing works
A novel gas/liquid two-phase flow imaging method through electrical resistance tomography with DDELM-AE sparse dictionary
Li, B., Wang, J. M., Wang, Q., Li, X. Y., & Duan, X. (2020) · Sensor Review · doi:10.1108/sr-01-2019-0018 · Cited by 9 · see citing works
Explaining Neural Networks by Decoding Layer Activations
Schneider, J., & Vlachos, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-74251-5_6 · Cited by 8 · see citing works
Multilayer discriminative extreme learning machine for classification
Lai, J., Wang, X., Xiang, Q., Song, Y., & Wen, Q. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01749-7 · Cited by 8 · see citing works
Simultaneously learning affinity matrix and data representations for machine fault diagnosis
Li, Y., Zeng, Y., Liu, T., Jia, X., & Huang, G.-B. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.11.007 · Cited by 8 · see citing works
Control and synchronization of chaotic spur gear system using adaptive non-singular fast terminal sliding mode controller
Khaniki, M. A. L., Kho, M. S., & Shoorehdeli, M. A. (2022) · Transactions of the Institute of Measurement and Control · doi:10.1177/01423312221087578 · Cited by 8 · see citing works
Minimum Variance Embedded Random Vector Functional Link Network with Privileged Information
Ganaie, M. A., Tanveer, M., Malik, A. K., & Suganthan, P. N. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891930 · Cited by 8 · see citing works
Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid
Mishra, S. P., Rayi, V. K., Dash, P. K., & Bisoi, R. (2021) · International Journal of Energy Research · doi:10.1002/er.7117 · Cited by 8 · see citing works
A Review of Neural Network Approach on Engineering Drawing Recognition and Future Directions
Yazed, M. S. M., Shaubari, E. F. A., & Yap, M. H. (2023) · JOIV International Journal on Informatics Visualization · doi:10.62527/joiv.7.4.1716 · Cited by 6 · see citing works
Consonant Phoneme Based Extreme Learning Machine (ELM) Recognition Model for Foreign Accent Identification
Kashif, K., Wu, Y., & Adjeisah, M. (2019) · World Symposium on Software Engineering (WSSE) · doi:10.1145/3362125.3362130 · Cited by 7 · see citing works
A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm
Satpathy, A., Dhar, S., Dash, P. K., Bisoi, R., & Nayak, N. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112185 · Cited by 7 · see citing works
Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive
Quezada-Gaibor, D., Klus, L., Klus, R., Lohan, E. S., Nurmi, J., Valkama, M., Huerta, J., & Torres-Sospedra, J. (2023) · IEEE Journal of Indoor and Seamless Positioning and Navigation · doi:10.1109/jispin.2023.3299433 · Cited by 7 · see citing works
A minimum entropy criterion for distribution selection for measurement uncertainty analysis
Huang, H. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad1476 · Cited by 7 · see citing works
A Machine Hearing Framework for Real-Time Streaming Analytics Using Lambda Architecture
Demertzis, K., Iliadis, L. S., & Anezakis, V.-D. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-20257-6_21 · Cited by 7 · see citing works
Multilayer Fisher extreme learning machine for classification
Lai, J., Wang, X., Xiang, Q., Wang, J., & Lei, L. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00867-7 · Cited by 6 · see citing works

Deep Representation Based on Multilayer Extreme Learning Machine
Qi, Y., & Li, Y. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-0740-8_17 · Cited by 1 · see citing works
Deep extreme learning machines: supervised autoencoding architecture for classification
Tissera, M. D., & McDonnell, M. D. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.110 · Cited by 131 · see citing works
Multilayer extreme learning machine: a systematic review
Kaur, R., Roul, R. K., & Batra, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14634-4 · Cited by 25 · see citing works
Extreme learning machines for feature learning
Lekamalage, C. K. L. (2017) · doi:10.32657/10356/69620
Supervised Extreme Learning Machine-Based Auto-Encoder for Discriminative Feature Learning
Du, J., Vong, C., Chen, C., Liu, P., & Liu, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2962067 · Cited by 13 · see citing works

Authors: Sun, K., Zhang, J., Zhang, C., & Hu, J.
Affiliations: Xi'an Jiaotong University (CN)
Corresponding author: Zhang, Jiangshe
ORCID: Sun, Kai
Zhang, Jiangshe
Zhang, Chunxia
Hu, Junying
Venue: Neurocomputing
Publisher: Elsevier BV
Published: 2017-03
Volume: 230
Pages: 374-381
ISSN: 0925-2312 (ISSN-L)
DOI: 10.1016/j.neucom.2016.12.027
OpenAlex: W2567582497
Cited by: 154
References: 33
Citations by year: 2026: 4 · 2025: 3 · 2024: 8 · 2023: 14 · 2022: 25 · 2021: 22 · 2020: 23 · 2019: 24 · 2018: 16 · 2017: 15
FWCI: 15.10
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Neurocomputing (publishedVersion)
Funders: National Basic Research Program of China (2013CB329404)
National Natural Science Foundation of China (61572393, 11501049, 11671317, 11131006)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords: extreme learning machine, autoencoder, manifold regularization, unsupervised feature extraction, deep neural network, stacked autoencoder

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Generalized extreme learning machine autoencoder and a new deep neural network

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks