The DOI matches its Crossref record; 31 of the 33 references it cites were found in a registry or an index.
Generalized extreme learning machine autoencoder and a new deep neural network
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“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]
Citations
Cites
Gradient-based learning applied to document recognition
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UCI Machine Learning Repository
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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
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
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
A tutorial on spectral clustering
Ulrike Von Luxburg, A. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,787
On Spectral Clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001) · Cited by 8,159
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562
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
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879
Research works printed but not linked: 1
- Kasun, 2013, Representational learning with ELMs for big data, IEEE Intell. Syst., 28, 31
Cited by
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
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020) · Materials · doi:10.3390/ma13245755 · Cited by 539
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 485
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
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
Self-citation
Metadata
- Authors
- Sun, K., Zhang, J., Zhang, C., & Hu, J.
- Affiliations
- Xi'an Jiaotong University (CN)
- Corresponding author
- Zhang, Jiangshe
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 230
- Pages
- 374-381
- ISSN
- 0925-2312 (ISSN-L)
- 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
- 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
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