Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.
References
[1]
Ezugwu, A.E., Ikotun, A.M., Oyelade, O.O., et al. (2022) A Comprehensive Survey of Clustering Algorithms: State-of-the-Art Machine Learning Applications, Taxonomy, Challenges, and Future Research Prospects. Engineering Applications of Artificial Intelligence, 110, Article 104743. https://doi.org/10.1016/j.engappai.2022.104743
[2]
Tieghi, L., Becker, S., Corsini, A., et al. (2023) Machine-Learning Clustering Methods Applied to Detection of Noise Sources in Low-Speed Axial Fan. Journal of Engineering for Gas Turbines and Power, 145, Article 031020. https://doi.org/10.1115/1.4055417
[3]
Kang, Z., Zhou, W., Zhao, Z., et al. (2020) Large-Scale Multi-View Subspace Clustering in Linear Time. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 4412-4419. https://doi.org/10.1609/aaai.v34i04.5867
[4]
Wu, H., Huang, S., Tang, C., et al. (2023) Pure Graph-Guided Multi-View Subspace Clustering. Pattern Recognition, 136, Article 109187. https://doi.org/10.1016/j.patcog.2022.109187
Zhao, N. and Bu, J. (2022) Robust Multi-View Subspace Clustering Based on Consensus Representation and Orthogonal Diversity. Neural Networks, 150, 102-111. https://doi.org/10.1016/j.neunet.2022.03.009
[7]
Zhu, W., Lu, J. and Zhou, J. (2019) Structured General and Specific Multi-View Subspace Clustering. Pattern Recognition, 93, 392-403. https://doi.org/10.1016/j.patcog.2019.05.005
[8]
Shi, Q., Hu, B., Zeng, T., et al. (2019) Multi-View Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data. Frontiers in Genetics, 10, Article 744. https://doi.org/10.3389/fgene.2019.00744
[9]
Liu, H., Shang, M., Zhang, H., et al. (2021) Cancer Subtype Identification Based on Multi-View Subspace Clustering with Adaptive Local Structure Learning. IEEE International Conference on Bio-Informatics and Biomedicine (BIBM), Houston, TX, 9-12 December 2021, 484-490. https://doi.org/10.1109/BIBM52615.2021.9669659
Tao, H., Hou, C., Qian, Y., et al. (2020) Latent Complete Row Space Recovery for Multi-View Subspace Clustering. IEEE Transactions on Image Processing, 29, 8083-8096. https://doi.org/10.1109/TIP.2020.3010631
[12]
Abavisani, M. and Patel, V.M. (2018) Multimodal Sparse and Low-Rank Subspace Clustering. Information Fusion, 39, 168-177. https://doi.org/10.1016/j.inffus.2017.05.002
[13]
Li, R., Zhang, C., Hu, Q., et al. (2019) Flexible Multi-View Representation Learning for Subspace Clustering. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China, 10-16 August 2019, 2916-2922. https://doi.org/10.24963/ijcai.2019/404
Zhang, G.-Y., Huang, D. and Wang, C.-D. (2023) Facilitated Low-Rank Multi-View Sub-Space Clustering. Knowledge-Based Systems, 260, Article 110141. https://doi.org/10.1016/j.knosys.2022.110141
[16]
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y. and Ma, Y. (2012) Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 171-184. https://doi.org/10.1109/TPAMI.2012.88
Du, S., Liu, B., Shan, G., et al. (2022) Enhanced Tensor Low-Rank Representation for Clustering and Denoising. Knowledge-Based Systems, 243, Article 108468. https://doi.org/10.1016/j.knosys.2022.108468
[19]
Wang, J., Zhu, L., Dai, T., et al. (2021) Low-Rank and Sparse Matrix Factorization with Prior Relations for Recommender Systems. Applied Intelligence, 51, 3435-3449. https://doi.org/10.1007/s10489-020-02023-5
[20]
Peng, J., Sun, W., Li, H.C., et al. (2021) Low-Rank and Sparse Representation for Hyperspectral Image Processing: A Review. IEEE Geoscience and Remote Sensing Magazine, 10, 10-43. https://doi.org/10.1109/MGRS.2021.3075491
[21]
Chen, J., Yang, S., Mao, H., et al. (2021) Multiview Subspace Clustering Using Low-Rank Representation. IEEE Transactions on Cybernetics, 52, 12364-12378. https://doi.org/10.1109/TCYB.2021.3087114
[22]
Hui, K., Shen, X., Abhadiomhen, S.E., et al. (2022) Robust Low-Rank Representation via Residual Projection for Image Classification. Knowledge-Based Systems, 241, Article 108230. https://doi.org/10.1016/j.knosys.2022.108230
[23]
Falsone, A., Notarnicola, I., Notarstefano, G., et al. (2020) Tracking-ADMM for Distributed Constraint-Coupled Optimization. Automatica, 117, Article 108962. https://doi.org/10.1016/j.automatica.2020.108962
[24]
Sun, Y., Yang, Y., Liu, Q., et al. (2020) Learning Non-Locally Regularized Compressed Sensing Network with Half-Quadratic Splitting. IEEE Transactions on Multimedia, 22, 3236-3248. https://doi.org/10.1109/TMM.2020.2973862
[25]
Cai, J.F., Candès, E.J. and Shen, Z. (2010) A Singular Value Thresholding Algorithm for Matrix Completion. SIAM Journal on Optimization, 20, 1956-1982. https://doi.org/10.1137/080738970
[26]
Sun, M., Zhang, P., Wang, S., Zhou, S., Tu, W. and Liu, X. (2021) Scalable Multi-View Subspace Clustering with Unified Anchors. Proceedings of the 29th ACM International Conference on Multimedia, China, 20-24 October 2021, 3528-3536 https://doi.org/10.1145/3474085.3475516
[27]
Wang, H., Yang, Y. and Liu, B (2020) GMC: Graph-Based Multi-View Clustering, IEEE Transactions on Knowledge and Data Engineering, 32, 1116-1129. https://doi.org/10.1109/TKDE.2019.2903810
[28]
Huang, L., Chao, H.Y. and Wang, C.D. (2019) Multi-View Intact Space Clustering. Pattern Recognition, 86, 344-353. https://doi.org/10.1016/j.patcog.2018.09.016