Heterogeneous clustering via adversarial deep Bayesian generative model

Abstract

This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep Bayesian clustering framework is proposed. In particular, a heterogeneous feature metric is first constructed to measure the similarity between different types of features. Then, a feature metric-restricted hierarchical sample generation process is established, in which sample with heterogeneous features is clustered by generating it from a similarity constraint hidden space. When estimating the model parameters and posterior probability, the corresponding variational inference algorithm is derived and implemented. To verify our model capability, we demonstrate our model on the synthetic dataset and show the superiority of the proposed method on some real datasets. Our source code is released on the website: Github.com/yexlwh/Heterogeneousclustering.

Graphical abstract

Keywords

dirichlet process / heterogeneous clustering / generative adversarial network / laplacian approximation / variational inference

Xulun Ye received the MSc and PhD degrees from Ningbo University, China in 2016 and 2019, respectively, where he is currently a lecturer. His research interests include Bayesian learning, deep learning, nonparametric clustering and convex analysis

Jieyu Zhao received the BS and MSc degrees from Zhejiang University, China and the PhD degree from Royal Holloway University of London, UK in 1985, 1988 and 1995 respectively. He is currently a full professor at Ningbo University, China. His research interests include deep learning, and computer vision

References

[1] Jiang Z, Zheng Y, Tan H, Tang B, Zhou H. Variational deep embedding: an unsupervised and generative approach to clustering. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 1965–1972

[2] Bhattacharjee P, Mitra P . A survey of density based clustering algorithms. Frontiers of Computer Science, 2021, 15( 1): 151308

[3] Xue H, Li S, Chen X, Wang Y . A maximum margin clustering algorithm based on indefinite kernels. Frontiers of Computer Science, 2019, 13( 4): 813–827

[4] Ghasedi K, Wang X, Deng C, Huang H. Balanced self-paced learning for generative adversarial clustering network. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 4386–4395

[5] Wen J, Zhang Z, Xu Y, Zhang B, Fei L, Xie G S. CDIMC-net: cognitive deep incomplete multi-view clustering network. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2021, 447

[6] Xie J, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis. In: Proceedings of the 3rd International Conference on Machine Learning. 2016, 478–487

[7] Zhou P, Hou Y, Feng J. Deep adversarial subspace clustering. In: Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018, 1596–1604

[8] Peng X, Xiao S, Feng J, Yau W Y, Yi Z. Deep subspace clustering with sparsity prior. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016, 1925–1931

[9] Guo X, Gao L, Liu X, Yin J. Improved deep embedded clustering with local structure preservation. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 1753–1759

[10] Ji P, Zhang T, Li H, Salzmann M, Reid I. Deep subspace clustering networks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 23–32

[11] Yu Y, Zhou W J. Mixture of GANs for clustering. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018, 3047–3053

[12] Yang X, Deng C, Zheng F, Yan J, Liu W. Deep spectral clustering using dual autoencoder network. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 4061–4070

[13] Shaham U, Stanton K P, Li H, Basri R, Nadler B, Kluger Y. SpectralNet: spectral clustering using deep neural networks. In: Proceedings of the 6th International Conference on Learning Representation. 2018

[14] Cheng J, Wang Q, Tao Z, Xie D, Gao Q. Multi-view attribute graph convolution networks for clustering. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2021, 411

[15] Menapace W, Lathuilière S, Ricci E. Learning to cluster under domain shift. In: Proceedings of the 16th European Conference on Computer Vision. 2020, 736–752

[16] Tapaswi M, Law M T, Fidler S. Video face clustering with unknown number of clusters. In: Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. 2019, 5026–5035

[17] Yang L, Zhan X, Chen D, Yan J, Boy C C, Lin D. Learning to cluster faces on an affinity graph. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 2293–2301

[18] Li J, Lu K, Huang Z, Zhu L, Shen H T . Heterogeneous domain adaptation through progressive alignment. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30( 5): 1381–1391

[19] Yang S, Song G, Jin Y, Du L. Domain adaptive classification on heterogeneous information networks. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2021, 196

[20] Wang C, Mahadevan S. Heterogeneous domain adaptation using manifold alignment. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 1541–1546

[21] Tsai Y H H, Yeh Y R, Wang Y C F. Learning cross-domain landmarks for heterogeneous domain adaptation. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016, 5081–5090

[22] Yeh Y R, Huang C H, Wang Y C F . Heterogeneous domain adaptation and classification by exploiting the correlation subspace. IEEE Transactions on Image Processing, 2014, 23( 5): 2009–2018

[23] Wang M, Deng W . Deep visual domain adaptation: a survey. Neurocomputing, 2018, 312: 135–153

[24] Day O, Khoshgoftaar T M . A survey on heterogeneous transfer learning. Journal of Big Data, 2017, 4( 1): 29

[25] Wang H, Yang Y, Liu B . GMC: graph-based multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 2020, 32( 6): 1116–1129

[26] Shi S, Nie F, Wang R, Li X. Fast multi-view clustering via prototype graph. IEEE Transactions on Knowledge and Data Engineering, 2021,

[27] Li Z, Nie F, Chang X, Yang Y, Zhang C, Sebe N . Dynamic affinity graph construction for spectral clustering using multiple features. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29( 12): 6323–6332

[28] Yin J, Sun S. Incomplete multi-view clustering with reconstructed views. IEEE Transactions on Knowledge and Data Engineering, 2021,

[29] Li L, Wan Z, He H. Incomplete multi-view clustering with joint partition and graph learning. IEEE Transactions on Knowledge and Data Engineering, 2021,

[30] Wang Y, Zhu J. DP-space: Bayesian nonparametric subspace clustering with small-variance asymptotics. In: Proceedings of the 32nd International Conference on Machine Learning. 2015, 862–870

[31] Gholami B, Pavlovic V. Probabilistic temporal subspace clustering. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 4313–4322

[32] Simo-Serra E, Torras C, Moreno-Noguer F . 3D human pose tracking priors using geodesic mixture models. International Journal of Computer Vision, 2017, 122( 2): 388–408

[33] Straub J, Freifeld O, Rosman G, Leonard J J, Fisher J W . The Manhattan frame model—Manhattan world inference in the space of surface normals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40( 1): 235–249

[34] Ye X, Zhao J . Multi-manifold clustering: a graph-constrained deep nonparametric method. Pattern Recognition, 2019, 93: 215–227

[35] Ye X, Zhao J, Zhang L, Guo L . A nonparametric deep generative model for multimanifold clustering. IEEE Transactions on Cybernetics, 2019, 49( 7): 2664–2677

[36] Hannah L A, Blei D M, Powell W B . Dirichlet process mixtures of generalized linear models. The Journal of Machine Learning Research, 2011, 12: 1923–1953

[37] Wang Y, Zhu J. Small-variance asymptotics for Dirichlet process mixtures of SVMs. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 2135–2141

[38] Blei D M, Jordan M I . Variational inference for Dirichlet process mixtures. Bayesian Analysis, 2006, 1( 1): 121–143

[39] Li Z, Cheong L F, Yang S, Toh K C . Simultaneous clustering and model selection: algorithm, theory and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40( 8): 1964–1978

[40] Liang J, Yang J, Cheng M M, Rosin P L, Wang L . Simultaneous subspace clustering and cluster number estimating based on triplet relationship. IEEE Transactions on Image Processing, 2019, 28( 8): 3973–3985

[41] Rodriguez A, Laio A . Clustering by fast search and find of density peaks. Science, 2014, 344( 6191): 1492–1496

[42] Ye X L, Zhao J, Chen Y, Guo L J . Bayesian adversarial spectral clustering with unknown cluster number. IEEE Transactions on Image Processing, 2020, 29: 8506–8518

[43] Mukherjee S, Asnani H, Lin E, Kannan S. ClusterGAN: latent space clustering in generative adversarial networks. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 4610–4617

[44] Chen W Y, Hsu T M H, Tsai Y H H, Wang Y C F, Chen M S. Transfer neural trees for heterogeneous domain adaptation. In: Proceedings of the 14th European Conference on Computer Vision. 2016, 399–414

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62006131, 62071260), the National Natural Science Foundation of Zhejiang Province (LQ21F020009, LQ18F020001).

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2023 Higher Education Press