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Robust deep multi-view subspace clustering networks with a correntropy-induced metric

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Abstract

Since multi-view subspace clustering combines the advantages of deep learning to capture the nonlinear nature of data, deep multi-view subspace clustering methods have demonstrated superior ability to shallow multi-view subspace clustering methods. Most existing methods assume that sample reconstruction errors incurred by noise conform to the prior distribution of the corresponding norm, allowing for simplification of the problem and focus on designing specific regularization on self-representation matrices to exploit consistent and diverse information among different views. However, the noise distributions in different views are always very complex, and in practice the noise distributions do not necessarily conform to this hypothesis. Furthermore, the commonly used diversity regularization based on value-awareness to enhance diversity among different view representations is not sufficiently accurate. To alleviate the above deficiencies, we propose novel robust deep multi-view subspace clustering networks with a correntropy-induced metric (RDMSCNet). (1) A correntropy-induced metric (CIM) is utilized to flexibly handle various complex noise distributions in a data-driven manner to improve the robustness of the model. (2) A position-aware diversity regularization based on the exclusivity definition is employed to enforce the diversity of the different view representations for modelling the consistency and diversity simultaneously. Extensive experiments show that RDMSCNet can deliver enhanced performance over state-of-the-art approaches.

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Notes

  1. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

  2. http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html.

  3. https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

  4. http://mlg.ucd.ie/datasets/segment.html.

  5. http://www.vision.caltech.edu/ImageDatasets/Caltech101/

References

  1. Sun X, He Z, Zhang X, Zou W, Baciu G (2016) Saliency detection via diversity-induced multi-view matrix decomposition. In: Asian conference on computer vision, pp 137–151

  2. Yao X, Han J, Zhang D, Nie F (2017) Revisiting co-saliency detection: a novel approach based on two-stage multi-view spectral rotation co-clustering. IEEE Trans Image Process 26(7):3196–3209

    Article  MathSciNet  Google Scholar 

  3. Zhang Q, Liu Y, Zhu S (2017) Salient object detection based on super-pixel clustering and unified low-rank representation. Comp Vision Image Underst 161:51–64

    Article  Google Scholar 

  4. Liu G, Lin Z, Yong Y (2010) Robust subspace segmentation by low-rank representation. In: International conference on machine learning pp 663–670

  5. Xia G, Sun H, Feng L, Zhang G, Liu Y (2017) Human motion segmentation via robust kernel sparse subspace clustering. IEEE Trans Image Process 27(1):135–150

    Article  MathSciNet  Google Scholar 

  6. Ji P, Zhong Y, Li H, Salzmann M (2014) Null space clustering with applications to motion segmentation and face clustering. In: IEEE International conference on image processing, pp 283–287

  7. Tulsiani S, Efros A, Malik J (2018) Multi-view consistency as supervisory signal for learning shape and pose prediction. In: IEEE conference on computer vision and pattern recognition, pp 2897–2905

  8. Wang X, Lei Z, Guo X, Zhang C, Shi H, Li SZ (2019) Multi-view subspace clustering with intactness-aware similarity. Pattern Recog 88:50–63

    Article  Google Scholar 

  9. Cao X, Zhang C, Zhou C, Fu H, Foroosh H (2015) Constrained multi-view video face clustering. IEEE Transactions on Image Processing 24(11):4381–4393

    Article  MathSciNet  Google Scholar 

  10. Si X, Yin Q, Zhao X, Yao L (2022) Consistent and diverse multi-view subspace clustering with structure constraint. Pattern Recogn, 121:108196

  11. Zhao J, Lyu G, Feng S (2021) Linear neighborhood reconstruction constrained latent subspace discovery for incomplete multi-view clustering. Applied Intelligence

  12. Zhang GY, Chen XW, Zhou YR, Wang CD, Huang D, He XY (2021) Kernelized multi-view subspace clustering via auto-weighted graph learning. Applied Intelligence

  13. Mi Y, Ren Z, Mukherjee M, Huang Y, Sun Q, Chen L (2021) Diversity and consistency embedding learning for multi-view subspace clustering. Applied Intelligence

  14. Zhu W, Lu J, Zhou J (2019) Structured general and specific multi-view subspace clustering. Pattern Recog 93:392–403

    Article  Google Scholar 

  15. Wang D, Yin Q, He R, Wang L, Tan T (2015) Multi-view Clustering via Structured Low-rank Representation. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 1911–1914

  16. Yin M, Liu W, Li M, Jin T, Ji R (2021) Cauchy loss induced block diagonal representation for robust multi-view subspace clustering. Neurocomputing 427:84–95

    Article  Google Scholar 

  17. Yin Q, Wu S, He R, Wang L (2015) Multi-view clustering via pairwise sparse subspace representation. Neurocomputing 156:12–21

    Article  Google Scholar 

  18. Elhamifar E, Vidal R (2009) Sparse subspace clustering. In: IEEE conference on computer vision and pattern recognition, pp 2790–2797

  19. Lu C, Min H, Zhao Z, Zhu L, Huang D-S, Yan S (2012) Robust and efficient subspace segmentation via least squares regression. In: European c on computer vision, pp 347–360

  20. Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: IEEE conference on computer vision and pattern recognition, pp 4279–4287

  21. Cao X, Zhang C, Fu H, Liu S, Zhang H (2015) Diversity-induced multi-view subspace clustering. In: IEEE conference on computer vision and pattern recognition, pp 586–594

  22. Gretton A, Bousquet O, Smola A, Schlkopf B (2005) Measuring statistical dependence with hilbert-schmidt norms. In: Algorithmic learning theory, 16th international conference, pp 63– 77

  23. Luo S, Zhang C, Zhang W, Cao X (2018) Consistent and specific multi-view subspace clustering. In: AAAI conference on artificial intelligence, pp 3730–3737

  24. Zhou T, Zhang C, Peng X, Bhaskar H, Yang J (2020) Dual shared-specific multiview subspace clustering. IEEE Transactions on Cybernetics 50(8):3517–3530

    Article  Google Scholar 

  25. Li R, Zhang C, Fu H, Peng X, Zhou TJ, Hu Q (2016) Reciprocal multi-layer subspace learning for multi-view clustering. In: IEEE international conference on computer vision, pp 8171– 8179

  26. Abavisani M, Patel VM (2018) Deep multimodal subspace clustering networks. IEEE Journal of Selected Topics in Signal Processing 12(6):1601–1614

    Article  Google Scholar 

  27. Lu R, Liu J, Zuo X (2021) Attentive multi-view deep subspace clustering net. Neurocomputing 435:186–196

    Article  Google Scholar 

  28. Zhu P, Hui B, Zhang C, Du D, Wen L, Hu Q (2019) Multi-view deep subspace clustering networks. arXiv

  29. Chao G, Sun J, Lu J, Wang A, Langleben D, Li C, Bi J (2019) Multi-view cluster analysis with incomplete data to understand treatment effects. Inf Sci 494:278–293

    Article  MathSciNet  Google Scholar 

  30. Yin Q, Zhang J, Wu S, Li H (2019) Multi-view clustering via joint feature selection and partially constrained cluster label learning. Pattern Recog 93:380–391

    Article  Google Scholar 

  31. Chao G, Sun S, Bi J (2021) A survey on multiview clustering. IEEE Trans Artif Intell 2 (2):146–168

    Article  Google Scholar 

  32. Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184

    Article  Google Scholar 

  33. Xia R, Pan Y, Du L, Yin J (2014) Robust multi-view spectral clustering via low-rank and sparse decomposition. In: AAAI Conference on Artificial Intelligence, pp 2149–2155

  34. Wang X, Guo X, Lei Z, Zhang C, Li S (2017) Exclusivity-consistency regularized multi-view subspace clustering. In: IEEE conference on computer vision and pattern recognition, pp 923–931

  35. Li C, Vidal R (2015) Structured sparse subspace clustering: A unified optimization framework. In: IEEE conference on computer vision and pattern recognition, pp 277–286

  36. Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems, pp 849–856

  37. Ye X, Wang L, Xing H, Huang L (2015) Denoising hybrid noises in image with stacked autoencoder. In: IEEE international conference on information and automation, pp 2720–2724

  38. Wei W, Yan H, Wang Y, Liang W (2014) Generalized autoencoder: A neural network framework for dimensionality reduction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 490–497

  39. Peng X, Xiao S, Feng J, Yuan W, Yi Z (2016) Deep subspace clustering with sparsity prior. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 1925–1931

  40. Ji P, Zhang T, Li H, Salzmann M, Reid I (2017) Deep subspace clustering networks. In: NIPS conference on neural information processing systems, pp 23–32

  41. Peng X, Feng J, Zhou JT, Lei Y, Yan S (2020) Deep subspace clustering. IEEE Trans Neural Netw Learn Syst 31(12):5509–5521

    Article  MathSciNet  Google Scholar 

  42. Elhamifar E, Vidal R (2013) Sparse subspace clustering: Algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781

    Article  Google Scholar 

  43. Huang Q, Zhang Y, Peng H, Dan T, Cai H (2020) Deep subspace clustering to achieve jointly latent feature extraction and discriminative learning. Neurocomputing 404:340–350

    Article  Google Scholar 

  44. Guo X (2016) Exclusivity regularized machine. arXiv:1603.08318

  45. Liu W, Pokharel PP, Principe JC (2007) Correntropy: Properties and applications in non-gaussian signal processing. IEEE Trans Sign Process 55(11):5286–5298

    Article  MathSciNet  Google Scholar 

  46. Chen B, Xing L, Zhao H, Zheng N, Prncipe JC (2016) Generalized correntropy for robust adaptive filtering. IEEE Trans Sign Process 64(13):3376–3387

    Article  MathSciNet  Google Scholar 

  47. Ma W, Qu H, Gui G, Xu L, Zhao J, Chen B (2015) Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-gaussian environments. J Frankl Inst 352(7):2708–2727

    Article  Google Scholar 

  48. Do B, Wang Z, Zhang L, Tao D (2017) Robust and discriminative labeling for multi-label active learning based on maximum correntropy criterion. IEEE Trans Image Process 26(4):1694–1707

    Article  MathSciNet  Google Scholar 

  49. Xu G, Du S, Xue J (2016) Precise 2d point set registration using iterative closest algorithm and correntropy. In: Proceedings of the International Joint Conference on Neural Networks, pp 4627–4631

  50. Cao Z, Principe JC, Ouyang B (2016) Information point set registration for shape recognition. In: IEEE international conference on acoustics, pp 2603–2607

  51. Kou Q, Cheng D, Chen L, Zhao K (2018) A multiresolution gray-scale and rotation invariant descriptor for texture classification. IEEE Access 6:30691–30701

    Article  Google Scholar 

  52. Lades M, Vorbruggen JC, Buhmann J, Lange J, Von Der Malsburg C, Wurtz RP, Konen W (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300–311

    Article  Google Scholar 

  53. Weng W, Zhou W, Chen J, Peng H, Cai H (2020) Enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures. Neurocomputing 378:375–386

    Article  Google Scholar 

  54. Jiang Y, Yang Z, Xu Q, Cao X, Huang Q (2018) When to learn what: Deep cognitive subspace clustering. In: Proceedings of the 26th ACM international conference on multimedia, pp 718–726

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Acknowledgements

This study was funded by the Key Program of National Natural Science Foundation of China (grant no. 61731003) and the Funds for National Natural Science Foundation of China (grant no. 61871040).

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Haidian, Beijing, China

    Xiaomeng Si, Xiaojie Zhao & Li Yao

  2. Institute of Automation, Chinese Academy of Sciences, Haidian, Beijing, China

    Qiyue Yin

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  1. Xiaomeng Si
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  2. Qiyue Yin
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  3. Xiaojie Zhao
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  4. Li Yao
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Correspondence to Li Yao.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Si, X., Yin, Q., Zhao, X. et al. Robust deep multi-view subspace clustering networks with a correntropy-induced metric. Appl Intell 52, 14871–14887 (2022). https://doi.org/10.1007/s10489-022-03209-9

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