Elastic deep multi-view autoencoder with diversity embedding

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

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works

GMC: Graph-Based Multi-View Clustering

Wang, H., Yang, Y. Z., & Liu, B. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2903810 · Cited by 964 · see citing works

Autoencoders and their applications in machine learning: a survey

Berahmand, K., Daneshfar, F., Salehi, E. S., Li, Y., & Xu, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10662-6 · Cited by 666 · see citing works

Binary Multi-View Clustering

Zhang, Z., Liu, L., Shen, F., et al. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2847335 · Cited by 602 · see citing works

Robust Multi-View Clustering With Incomplete Information

Yang, M., Li, Y., Hu, P., Bai, J., Lv, J., & Peng, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3155499 · Cited by 359 · see citing works

A Comprehensive Survey on Multi-View Clustering

Fang, U., Li, M., Li, J., et al. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3270311 · Cited by 351 · see citing works

Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity

Huang, D., Wang, C., & Lai, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3236698 · Cited by 276 · see citing works

Deep embedded multi-view clustering with collaborative training

Xu, J., Ren, Y., Li, G., Pan, L., Zhu, C., & Xu, Z. (2021) · Information Sciences · doi:10.1016/j.ins.2020.12.073 · Cited by 273 · see citing works

A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection

Fanai, H., & Abbasimehr, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119562 · Cited by 184 · see citing works

Adaptive Feature Projection With Distribution Alignment for Deep Incomplete Multi-View Clustering

Xu, J., Li, C., Peng, L., et al. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3243521 · Cited by 183 · see citing works

Joint stage recognition and anatomical annotation of drosophila gene expression patterns

Cai, X., Wang, H., Huang, H., & Ding, C. H. Q. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts220 · Cited by 166 · see citing works

Twin Contrastive Learning for Online Clustering

Li, Y., Yang, M., Peng, D., Li, T., Huang, J., & Peng, X. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01639-z · Cited by 152 · see citing works

Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

Li, C., Che, H., Leung, M.-F., et al. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.119 · Cited by 115 · see citing works

Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering

Wang, S., Li, C., Li, Y., Yuan, Y., & Wang, G. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3246802 · Cited by 108 · see citing works

Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A survey

Dol, S. M., & Jawandhiya, P. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106071 · Cited by 99 · see citing works

Neighbor-aware deep multi-view clustering via graph convolutional network

Du, G., Zhou, L., Li, Z., Wang, L., & Lü, K. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.01.001 · Cited by 93 · see citing works

Comprehensive Multi-view Representation Learning

Zheng, Q., Zhu, J., Li, Z., Tian, Z., & Li, C. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.08.014 · Cited by 87 · see citing works

Facilitated low-rank multi-view subspace clustering

Zhang, G., Huang, D., & Wang, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110141 · Cited by 82 · see citing works

Collaborative structure and feature learning for multi-view clustering

Yan, W., Gu, M., Ren, J., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101832 · Cited by 76 · see citing works

Unsupervised Multi-View K-Means Clustering Algorithm

Yang, M., & Hussain, I. (2023) · IEEE Access · doi:10.1109/access.2023.3243133 · Cited by 68 · see citing works

A survey on semi-supervised graph clustering

Daneshfar, F., Soleymanbaigi, S., Yamini, P., & Amini, M. S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108215 · Cited by 67 · see citing works

Self-paced and auto-weighted multi-view clustering

Ren, Y., Huang, S., Zhao, P., et al. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.11.104 · Cited by 63 · see citing works

Self-taught Multi-view Spectral Clustering

Zhong, G., & Pun, C. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109349 · Cited by 56 · see citing works

Collaborative Unsupervised Multi-View Representation Learning

Zheng, Q., Zhu, J., & Li, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3127007 · Cited by 53 · see citing works

Robust multi-view subspace clustering based on consensus representation and orthogonal diversity

Zhao, N., & Bu, J. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.009 · Cited by 53 · see citing works

Graph clustering network with structure embedding enhanced

Ding, S., Wu, B., Wu, B., et al. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109833 · Cited by 53 · see citing works

Dual self-paced multi-view clustering

Huang, Z., Ren, Y., Pu, X., et al. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.02.022 · Cited by 52 · see citing works

Auto-weighted orthogonal and nonnegative graph reconstruction for multi-view clustering

Zhao, M., Yang, W., & Nie, F. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.016 · Cited by 50 · see citing works

Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis

Yan, J., Chen, H., Li, X., & Yao, J. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102053 · Cited by 48 · see citing works

LyEmoBERT: Classification of lyrics’ emotion and recommendation using a pre-trained model

Revathy, V. R., Pillai, A. S., & Daneshfar, F. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.098 · Cited by 45 · see citing works

Auto-weighted collective matrix factorization with graph dual regularization for multi-view clustering

Liu, M., Yang, Z., Li, L., et al. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110145 · Cited by 42 · see citing works

Iterative clustering pruning for convolutional neural networks

Chang, J., Lu, Y., Xue, P., Xu, Y., & Wei, Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110386 · Cited by 42 · see citing works

Elastic deep autoencoder for text embedding clustering by an improved graph regularization

Daneshfar, F., Soleymanbaigi, S., Nafisi, A., & Yamini, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121780 · Cited by 41 · see citing works

Graph-Guided Unsupervised Multiview Representation Learning

Zheng, Q., Zhu, J., Li, Z., & Tang, H. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3200451 · Cited by 37 · see citing works

Dynamic auto-weighted multi-view co-clustering

Hu, S., Yan, X., & Ye, Y. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107101 · Cited by 31 · see citing works

Deep multi-view spectral clustering via ensemble

Zhao, M., Yang, W., & Nie, F. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109836 · Cited by 31 · see citing works

MCoCo: Multi-level Consistency Collaborative multi-view clustering

Zhou, Y., Zheng, Q., Wang, Y., Yan, W., Shi, P., & Zhu, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121976 · Cited by 31 · see citing works

Deep embedded clustering with distribution consistency preservation for attributed networks

Zheng, Y., Jia, C., Yu, J. J., et al. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109469 · Cited by 28 · see citing works

Sensitive Electrochemical Detection of Bisphenol A Using Molybdenum Disulfide/Au Nanorod Composites Modified Glassy Carbon Electrode

Wang, M., Shi, Y., Zhang, Y., Wang, Y., Huang, H., Zhang, J., & Song, J. (2017) · Electroanalysis · doi:10.1002/elan.201700411 · Cited by 26 · see citing works

Few-shot relation classification using clustering-based prototype modification

Wen, M., Xia, T., Liao, B., et al. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110477 · Cited by 22 · see citing works

Center consistency guided multi-view embedding anchor learning for large-scale graph clustering

Zhang, X., Ren, Z., & Yang, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110162 · Cited by 19 · see citing works

Single phase multi-view clustering using unified graph learning and spectral representation

Dornaika, F., & Hajjar, S. E. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119366 · Cited by 12 · see citing works

Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

Shi, Z., & Zhao, H. (2023) · Applied Sciences · doi:10.3390/app13158791 · Cited by 10 · see citing works

Multimodal fusion and vision–language models: A survey for robot vision

Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works

A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on Space Payload

Du, J., Song, L., Zheng, T., et al. (2020) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa.2019.00041

Performance Comparison of Three Types of Autoencoder Neural Networks

Tan, C. C., & Eswaran, C. (2008) · Asia International Conference on Modelling & Simulation (AMS) · doi:10.1109/ams.2008.105 · Cited by 29 · see citing works

The Learning Effect of Different Hidden Layers Stacked Autoencoder

Xu, Q., Zhang, C., Zhang, L., et al. (2016) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2016.280 · Cited by 21 · see citing works

Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions

Utkin, L. V., DROBINTSEV, P. D., Kovalev, M. S., & Konstantinov, A. V. (2021) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct50888.2021.9347612 · Cited by 5 · see citing works

The difference learning of hidden layer between autoencoder and variational autoencoder

Xu, Q., Wu, Z., Yang, Y., et al. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7979344 · Cited by 27 · see citing works