Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning (2401.12648v3)

Published 23 Jan 2024 in cs.LG and cs.CV

Abstract: Multiview clustering (MVC) segregates data samples into meaningful clusters by synthesizing information across multiple views. Moreover, deep learning-based methods have demonstrated their strong feature learning capabilities in MVC scenarios. However, effectively generalizing feature representations while maintaining consistency is still an intractable problem. In addition, most existing deep clustering methods based on contrastive learning overlook the consistency of the clustering representations during the clustering process. In this paper, we show how the above problems can be overcome and propose a consistent enhancement-based deep MVC method via contrastive learning (CCEC). Specifically, semantic connection blocks are incorporated into a feature representation to preserve the consistent information among multiple views. Furthermore, the representation process for clustering is enhanced through spectral clustering, and the consistency across multiple views is improved. Experiments conducted on five datasets demonstrate the effectiveness and superiority of our method in comparison with the state-of-the-art (SOTA) methods. The code for this method can be accessed at https://anonymous.4open.science/r/CCEC-E84E/.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. Uci machine learning repository, 2007.
  2. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, 33:9912–9924, 2020.
  3. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597–1607. PMLR, 2020.
  4. Multiview subspace clustering using low-rank representation. IEEE Transactions on Cybernetics, 52(11):12364–12378, 2022.
  5. Augmented sparse representation for incomplete multiview clustering. IEEE Transactions on Neural Networks and Learning Systems, pages 1–14, 2022.
  6. Multiview clustering by consensus spectral rotation fusion. IEEE Transactions on Image Processing, 32:5153–5166, 2023.
  7. Deep multiview clustering by contrasting cluster assignments. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 16752–16761, October 2023.
  8. Low-rank tensor learning for incomplete multiview clustering. IEEE Transactions on Knowledge and Data Engineering, 35(11):11556–11569, 2023.
  9. Online sparse representation clustering for evolving data streams. IEEE Transactions on Neural Networks and Learning Systems, pages 1–15, 2023.
  10. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop, pages 178–178. IEEE, 2004.
  11. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), volume 2, pages 1735–1742. IEEE, 2006.
  12. Trusted multi-view classification with dynamic evidential fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2551–2566, 2023.
  13. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  14. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729–9738, 2020.
  15. One-pass incomplete multi-view clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):3838–3845, Jul. 2019.
  16. Multi-view spectral clustering network. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pages 2563–2569. International Joint Conferences on Artificial Intelligence Organization, 7 2019.
  17. Incomplete multi-view clustering with joint partition and graph learning. IEEE Transactions on Knowledge and Data Engineering, 35(1):589–602, 2023.
  18. Graph filter-based multi-view attributed graph clustering. In IJCAI, pages 2723–2729, 2021.
  19. Dual contrastive prediction for incomplete multi-view representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4447–4461, 2022.
  20. Late fusion incomplete multi-view clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(10):2410–2423, 2019.
  21. Efficient and effective regularized incomplete multi-view clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(8):2634–2646, 2021.
  22. Ishan Misra and Laurens van der Maaten. Self-supervised learning of pretext-invariant representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
  23. Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision, 42:145–175, 2001.
  24. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
  25. Multi-view contrastive graph clustering. Advances in neural information processing systems, 34:2148–2159, 2021.
  26. Comic: Multi-view clustering without parameter selection. In International conference on machine learning, pages 5092–5101. PMLR, 2019.
  27. Deep safe incomplete multi-view clustering: Theorem and algorithm. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 21090–21110. PMLR, 17–23 Jul 2022.
  28. Deep safe multi-view clustering: Reducing the risk of clustering performance degradation caused by view increase. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 202–211, June 2022.
  29. Unified one-step multi-view spectral clustering. IEEE Transactions on Knowledge and Data Engineering, 35(6):6449–6460, 2023.
  30. From ensemble clustering to subspace clustering: Cluster structure encoding. IEEE Transactions on Neural Networks and Learning Systems, 34(5):2670–2681, 2023.
  31. Contrastive multiview coding. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16, pages 776–794. Springer, 2020.
  32. Reconsidering representation alignment for multi-view clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1255–1265, 2021.
  33. Self-supervised learning from a multi-view perspective. arXiv preprint arXiv:2006.05576, 2020.
  34. Scan: Learning to classify images without labels. In European conference on computer vision, pages 268–285. Springer, 2020.
  35. Discrete multiple kernel k-means. In IJCAI, pages 3111–3117, 2021.
  36. Locus: Learning object classes with unsupervised segmentation. In Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1, volume 1, pages 756–763. IEEE, 2005.
  37. Multi-level feature learning for contrastive multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16051–16060, June 2022.
  38. Self-supervised discriminative feature learning for deep multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 35(7):7470–7482, 2023.
  39. Gcfagg: Global and cross-view feature aggregation for multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19863–19872, June 2023.
  40. Dealmvc: Dual contrastive calibration for multi-view clustering. In Proceedings of the 31st ACM International Conference on Multimedia, pages 337–346, 2023.
  41. Multiview concept learning via deep matrix factorization. IEEE Transactions on Neural Networks and Learning Systems, 32(2):814–825, 2021.
  42. Deep multi-view spectral clustering via ensemble. Pattern Recognition, 144:109836, 2023.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Hao Yang (328 papers)
  2. Hua Mao (8 papers)
  3. Wai Lok Woo (13 papers)
  4. Jie Chen (602 papers)
  5. Xi Peng (115 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets