research-article

Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information

Authors:

Qi LiuAuthors Info & Claims

Volume 179, Issue C

https://doi.org/10.1016/j.neunet.2024.106563

Published: 01 November 2024 Publication History

Abstract

In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by approximating the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework’s efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms.

References

[1]

Chen, M.-S., Huang, L., Wang, C.-D., & Huang, D. (2020). Multi-view clustering in latent embedding space. Vol. 34, In Proceedings of the AAAI conference on artificial intelligence (04), (pp. 3513–3520).

[2]

Chen M.-S., Huang L., Wang C.-D., Huang D., Lai J.-H., Relaxed multi-view clustering in latent embedding space, Information Fusion 68 (2021) 8–21.

[3]

Chen T., Kornblith S., Norouzi M., Hinton G., A simple framework for contrastive learning of visual representations, in: International conference on machine learning, PMLR, 2020, pp. 1597–1607.

[4]

Cheng, J., Wang, Q., Tao, Z., Xie, D., & Gao, Q. (2021). Multi-view attribute graph convolution networks for clustering. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence (pp. 2973–2979).

[5]

Gao, Q., Xia, W., Wan, Z., Xie, D., & Zhang, P. (2020). Tensor-SVD based graph learning for multi-view subspace clustering. Vol. 34, In Proceedings of the AAAI conference on artificial intelligence (04), (pp. 3930–3937).

[6]

Hassani K., Khasahmadi A.H., Contrastive multi-view representation learning on graphs, in: International conference on machine learning, PMLR, 2020, pp. 4116–4126.

[7]

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9729–9738).

[8]

Hinton G.E., Training products of experts by minimizing contrastive divergence, Neural Computation 14 (8) (2002) 1771–1800.

Digital Library

[9]

Hinton G.E., Salakhutdinov R.R., Reducing the dimensionality of data with neural networks, Science 313 (5786) (2006) 504–507.

[10]

Huang S., Kang Z., Xu Z., Auto-weighted multi-view clustering via deep matrix decomposition, Pattern Recognition 97 (2020).

Digital Library

[11]

Kang, Z., Zhou, W., Zhao, Z., Shao, J., Han, M., & Xu, Z. (2020). Large-scale multi-view subspace clustering in linear time. Vol. 34, In Proceedings of the AAAI conference on artificial intelligence (04), (pp. 4412–4419).

[12]

Khajwal A.B., Cheng C.-S., Noshadravan A., Post-disaster damage classification based on deep multi-view image fusion, Computer-Aided Civil and Infrastructure Engineering 38 (4) (2023) 528–544.

[13]

Kingma D.P., Ba J., Adam: A method for stochastic optimization, 2014, arXiv preprint arXiv:1412.6980.

[14]

Li, Y., Ge, Z., Yu, G., Yang, J., Wang, Z., Shi, Y., et al. (2023). Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. Vol. 37, In Proceedings of the AAAI conference on artificial intelligence (2), (pp. 1477–1485).

[15]

Li Z., Tang C., Liu X., Zheng X., Zhang W., Zhu E., Consensus graph learning for multi-view clustering, IEEE Transactions on Multimedia 24 (2021) 2461–2472.

[16]

Li Z., Wang Q., Tao Z., Gao Q., Yang Z., et al., Deep adversarial multi-view clustering network, in: IJCAI, 2019, pp. 2952–2958.

[17]

Lin, Y., Gou, Y., Liu, Z., Li, B., Lv, J., & Peng, X. (2021). Completer: Incomplete multi-view clustering via contrastive prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11174–11183).

[18]

Liu X., Incomplete multiple kernel alignment maximization for clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).

Digital Library

[19]

Liu X., Simplemkkm: Simple multiple kernel k-means, IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).

[20]

Liu L., Chen P., Luo G., Kang Z., Luo Y., Han S., Scalable multi-view clustering with graph filtering, Neural Computing and Applications 34 (19) (2022) 16213–16221.

[21]

Liu, C., Liao, Z., Ma, Y., & Zhan, K. (2022). Stationary diffusion state neural estimation for multiview clustering. Vol. 36, In Proceedings of the AAAI conference on artificial intelligence (7), (pp. 7542–7549).

[22]

Liu X., Liu L., Liao Q., Wang S., Zhang Y., Tu W., et al., One pass late fusion multi-view clustering, in: International conference on machine learning, PMLR, 2021, pp. 6850–6859.

[23]

Liu S., Liu X., Wang S., Niu X., Zhu E., Fast incomplete multi-view clustering with view-independent anchors, IEEE Transactions on Neural Networks and Learning Systems (2022).

[24]

Liu J., Wang C., Gao J., Han J., Multi-view clustering via joint nonnegative matrix factorization, in: Proceedings of the 2013 SIAM international conference on data mining, SIAM, 2013, pp. 252–260.

[25]

Liu, S., Wang, S., Zhang, P., Xu, K., Liu, X., Zhang, C., et al. (2022). Efficient one-pass multi-view subspace clustering with consensus anchors. Vol. 36, In Proceedings of the AAAI conference on artificial intelligence (7), (pp. 7576–7584).

[26]

Peng X., Huang Z., Lv J., Zhu H., Zhou J.T., COMIC: Multi-view clustering without parameter selection, in: International conference on machine learning, PMLR, 2019, pp. 5092–5101.

[27]

Shah, K., Shah, A., Lau, C. P., de Melo, C. M., & Chellappa, R. (2023). Multi-view action recognition using contrastive learning. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 3381–3391).

[28]

Song J., Zhang H., Li X., Gao L., Wang M., Hong R., Self-supervised video hashing with hierarchical binary auto-encoder, IEEE Transactions on Image Processing 27 (7) (2018) 3210–3221.

[29]

Tang, H., & Liu, Y. (2022). 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 (pp. 202–211).

[30]

Tian Y., Krishnan D., Isola P., Contrastive multiview coding, in: Computer vision–ECCV 2020: 16th European conference, glasgow, UK, August 23–28, 2020, proceedings, part XI 16, Springer, 2020, pp. 776–794.

[31]

Trosten, D. J., Lokse, S., Jenssen, R., & Kampffmeyer, M. (2021). Reconsidering representation alignment for multi-view clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1255–1265).

[32]

Van den Oord A., Li Y., Vinyals O., et al., Representation learning with contrastive predictive coding, 2018, p. 4. arXiv preprint arXiv:1807.03748.

[33]

Van der Maaten L., Hinton G., Visualizing data using t-SNE, Journal of Machine Learning Research 9 (11) (2008).

[34]

Wang S., Liu X., Liu S., Jin J., Tu W., Zhu X., et al., Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences, Advances in Neural Information Processing Systems 35 (2022) 5882–5895.

[35]

Wang S., Liu X., Liu L., Zhou S., Zhu E., Late fusion multiple kernel clustering with proxy graph refinement, IEEE Transactions on Neural Networks and Learning Systems (2021).

[36]

Wang S., Liu X., Zhu X., Zhang P., Zhang Y., Gao F., et al., Fast parameter-free multi-view subspace clustering with consensus anchor guidance, IEEE Transactions on Image Processing 31 (2021) 556–568.

[37]

Wang, S., Zhao, X., Xu, H.-M., Chen, Z., Yu, D., Chang, J., et al. (2023). Towards domain generalization for multi-view 3d object detection in bird-eye-view. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13333–13342).

[38]

Wen, J., Zhang, Z., Xu, Y., & Zhong, Z. (2018). Incomplete multi-view clustering via graph regularized matrix factorization. In Proceedings of the European conference on computer vision (ECCV) workshops.

[39]

Xia W., Gao Q., Wang Q., Gao X., Ding C., Tao D., Tensorized bipartite graph learning for multi-view clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4) (2022) 5187–5202.

[40]

Xia W., Wang Q., Gao Q., Zhang X., Gao X., Self-supervised graph convolutional network for multi-view clustering, IEEE Transactions on Multimedia 24 (2021) 3182–3192.

[41]

Xia W., Wang S., Yang M., Gao Q., Han J., Gao X., Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation, Neural Networks 145 (2022) 1–9.

[42]

Xiong, K., Gong, S., Ye, X., Tan, X., Wan, J., Ding, E., et al. (2023). Cape: Camera view position embedding for multi-view 3d object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 21570–21579).

[43]

Xu J., Ren Y., Li G., Pan L., Zhu C., Xu Z., Deep embedded multi-view clustering with collaborative training, Information Sciences 573 (2021) 279–290.

[44]

Xu, J., Ren, Y., Tang, H., Pu, X., Zhu, X., Zeng, M., et al. (2021). Multi-VAE: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9234–9243).

[45]

Xu J., Ren Y., Tang H., Yang Z., Pan L., Yang Y., et al., Self-supervised discriminative feature learning for deep multi-view clustering, IEEE Transactions on Knowledge and Data Engineering (2022).

Digital Library

[46]

Xu, J., Tang, H., Ren, Y., Peng, L., Zhu, X., & He, L. (2022). Multi-level feature learning for contrastive multi-view clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 16051–16060).

[47]

Xu J., Xu K., Chen K., Ruan J., Reweighted sparse subspace clustering, Computer Vision and Image Understanding 138 (2015) 25–37.

[48]

Xu J., Yu M., Shao L., Zuo W., Meng D., Zhang L., et al., Scaled simplex representation for subspace clustering, IEEE Transactions on Cybernetics 51 (3) (2019) 1493–1505.

[49]

Yang X., Hu X., Zhou S., Liu X., Zhu E., Interpolation-based contrastive learning for few-label semi-supervised learning, IEEE Transactions on Neural Networks and Learning Systems (2022) 1–12,.

[50]

Yang, X., Jin, J., Wang, S., Liang, K., Liu, Y., Wen, Y., et al. (2023). DealMVC: Dual Contrastive Calibration for Multi-view Clustering. In Proceedings of the 31th ACM international conference on multimedia.

[51]

Yang X., Liu Y., Zhou S., Liu X., Zhu E., Mixed graph contrastive network for semi-supervised node classification, 2022, arXiv preprint arXiv:2206.02796.

[52]

Yang X., Liu Y., Zhou S., Wang S., Liu X., Zhu E., Contrastive deep graph clustering with learnable augmentation, 2022, arXiv preprint arXiv:2212.03559.

[53]

Yang, X., Liu, Y., Zhou, S., Wang, S., Tu, W., Zheng, Q., et al. (2023). Cluster-guided Contrastive Graph Clustering Network. Vol. 37, In Proceedings of the AAAI conference on artificial intelligence (9), (pp. 10834–10842).

[54]

Yang, X., Tan, C., Liu, Y., Liang, K., Wang, S., Zhou, S., et al. (2023). CONVERT: Contrastive Graph Clustering with Reliable Augmentation. In Proceedings of the 31th ACM international conference on multimedia.

[55]

Zbontar J., Jing L., Misra I., LeCun Y., Deny S., Barlow twins: Self-supervised learning via redundancy reduction, 2021, arXiv preprint arXiv:2103.03230.

[56]

Zhang, C., Liu, Y., & Fu, H. (2019). Ae2-nets: Autoencoder in autoencoder networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2577–2585).

[57]

Zhang, X.-L., & Wang, D. (2014). Boosted deep neural networks and multi-resolution cochleagram features for voice activity detection. In Fifteenth annual conference of the international speech communication association.

[58]

Zhang X.-L., Wang D., Boosting contextual information for deep neural network based voice activity detection, IEEE/ACM Transactions on Audio, Speech, and Language Processing 24 (2) (2015) 252–264.

[59]

Zhang X.-L., Wang D., A deep ensemble learning method for monaural speech separation, IEEE/ACM Transactions on Audio, Speech, and Language Processing 24 (5) (2016) 967–977.

[60]

Zhang X.-L., Wu J., Deep belief networks based voice activity detection, IEEE Transactions on Audio, Speech, and Language Processing 21 (4) (2012) 697–710.

[61]

Zhao, H., Ding, Z., & Fu, Y. (2017). Multi-view clustering via deep matrix factorization. Vol. 31, In Proceedings of the AAAI conference on artificial intelligence. (1).

[62]

Zhou S., Liu X., Li M., Zhu E., Liu L., Zhang C., et al., Multiple kernel clustering with neighbor-kernel subspace segmentation, IEEE Transactions on Neural Networks and Learning Systems 31 (4) (2019) 1351–1362.

[63]

Zhou, R., & Shen, Y.-D. (2020). End-to-end adversarial-attention network for multi-modal clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 14619–14628).

[64]

Zhou S., Zhu E., Liu X., Zheng T., Liu Q., Xia J., et al., Subspace segmentation-based robust multiple kernel clustering, Information Fusion 53 (2020) 145–154.

Index Terms

Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information

Index terms have been assigned to the content through auto-classification.

Recommendations

Contrastive and attentive graph learning for multi-view clustering
Abstract
Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies ...
Highlights
- A novel CAGL framework for multi-view clustering is proposed.
- CL is used to ...
One-step multi-view clustering guided by weakened view-specific distribution
Abstract
In recent years, multi-view clustering has demonstrated superior performance by capturing complementary information from diverse views, garnering considerable attention. Although existing methods have shown promising results, they encounter some ...
Double-level View-correlation Multi-view Subspace Clustering
Abstract
In recent years, significant progress has been made in Multi-view Subspace Clustering (MSC). Most existing MSC methods attempt to explore and exploit the view correlations of multi-view data to boost the clustering performance. They achieve the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Neural Networks

Neural Networks Volume 179, Issue C

Nov 2024

1557 pages

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 November 2024

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents