[17] Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. Variational deep embedding: an unsupervised and generative approach to clustering. In *IJCAI*, pages 1965–1972, 2017.
[18] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
[19] Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. Semi-supervised learning with deep generative models. In NeurIPS, pages 3581-3589, 2014.
[20] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86:2278-2324, 1998.
[21] Chung-Kuei Lee and Tyng-Luh Liu. Guided co-training for multi-view spectral clustering. In ICIP, pages 4042-4046, 2016.
[22] Ruihuang Li, Changqing Zhang, Huazhu Fu, Xi Peng, Tianyi Zhou, and Qinghua Hu. Reciprocal multi-layer subspace learning for multi-view clustering. In ICCV, pages 8172-8180, 2019.
[23] Zhaoyang Li, Qianqian Wang, Zhiqiang Tao, Quanxue Gao, Zhaohua Yang, et al. Deep adversarial multi-view clustering network. In IJCAI, pages 2952-2958, 2019.
[24] Jialu Liu, Chi Wang, Jing Gao, and Jiawei Han. Multi-view clustering via joint nonnegative matrix factorization. In SDM, pages 252-260, 2013.
[25] Yunxiao Liu, Youfang Lin, QinFeng Xiao, Ganghui Hu, and Jing Wang. Self-adversarial variational autoencoder with spectral residual for time series anomaly detection. Neurocomputing, 458:349-363, 2021.
[26] Can-Yi Lu, Hai Min, Zhong-Qiu Zhao, Lin Zhu, De-Shuang Huang, and Shuicheng Yan. Robust and efficient subspace segmentation via least squares regression. In ECCV, pages 347-360. Springer, 2012.
[27] James MacQueen. Classification and analysis of multivariate observations. In BSMSP, pages 281-297, 1967.
[28] Jonathan Masci, Ueli Meier, Dan Ciresan, and Jürgen Schmidhuber. Stacked convolutional auto-encoders for hierarchical feature extraction. In ICANN, pages 52–59, 2011.
[29] Sameer A Nene, Shree K Nayar, Hiroshi Murase, et al. Columbia object image library (coil-20). Technical Report, CUCS-005-96, 1996.
[30] Feiping Nie, Hua Wang, Heng Huang, and Chris Ding. Unsupervised and semi-supervised learning via  $l$  1-norm graph. In ICCV, pages 2268-2273. IEEE, 2011.
[31] Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, and Luke Zettlemoyer. An information bottleneck approach for controlling conciseness in rationale extraction. In EMNLP, pages 1938-1952, 2020.
[32] Yazhou Ren, Shudong Huang, Peng Zhao, Minghao Han, and Zenglin Xu. Self-paced and auto-weighted multi-view clustering. Neurocomputing, 383:248-256, 2020.
[33] Andrew M Saxe, James L McClelland, and Surya Ganguli. A mathematical theory of semantic development in deep neural networks. Proceedings of the National Academy of Sciences, 116:11537-11546, 2019.
[34] Ravid Shwartz-Ziv and Naftali Tishby. Opening the black box of deep neural networks via information. Information Flow in Deep Neural Networks, page 24, 2022.
[35] Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and Philip S Yu. Graph structure learning with variational information bottleneck. In AAAI, pages 4165-4174, 2022.
[36] N TISHBY. The information bottleneck method. Computing Research Repository (CoRR), 2000.