research-article

Deep Multi-view Sparse Subspace Clustering

Authors:

Xiaoliang Tang,

Xian WeiAuthors Info & Claims

ICNCC '18: Proceedings of the 2018 VII International Conference on Network, Communication and Computing

Pages 115 - 119

https://doi.org/10.1145/3301326.3301391

Published: 14 December 2018 Publication History

Abstract

Most multi-view subspace clustering algorithms construct the affinity matrix with shallow features extracted from each view separately. The integration of multi-view features are left for extended spectral clustering algorithm. The lack of deep feature extraction and interaction across different views prevents the effective exploration of information complementary for multi-view datasets. To address this problem, this paper proposes a novel deep multi-view sparse subspace clustering (DMVSSC) model which consists of convolutional auto-encoders (CAEs) and CCA-based self-expressive module. The proposed model can not only extract deep features of each view data with few parameters but also integrate multi-view features based on CCA. Furthermore, a two-stage joint optimization strategy is proposed for tuning the whole model. Experiments on four benchmark data sets show that our proposed model significantly outperforms the state-of-the-art multi-view subspace clustering algorithms.

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Bai RHuang RXu LQin Y(2025)CSMDC: Exploring consistently context semantics for multi-view document clusteringExpert Systems with Applications10.1016/j.eswa.2024.125386261(125386)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125386
Zhang FChe H(2024)Separable Consistency and Diversity Feature Learning for Multi-View ClusteringIEEE Signal Processing Letters10.1109/LSP.2024.340860631(1595-1599)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3408606
Yang ZZhang HWei YWang ZNie FHu D(2024)Geometric-inspired graph-based Incomplete Multi-view ClusteringPattern Recognition10.1016/j.patcog.2023.110082147(110082)Online publication date: Mar-2024
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Index Terms

Deep Multi-view Sparse Subspace Clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image ACM Other conferences

ICNCC '18: Proceedings of the 2018 VII International Conference on Network, Communication and Computing

December 2018

372 pages

ISBN:9781450365536

DOI:10.1145/3301326

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 December 2018

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CAS Pioneer Hundred Talents Program (Type C)
National Science Found for Young Scholars

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ICNCC 2018

ICNCC 2018: 2018 VII International Conference on Network, Communication and Computing

December 14 - 16, 2018

Taipei City, Taiwan

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Cited By

Bai RHuang RXu LQin Y(2025)CSMDC: Exploring consistently context semantics for multi-view document clusteringExpert Systems with Applications10.1016/j.eswa.2024.125386261(125386)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125386
Zhang FChe H(2024)Separable Consistency and Diversity Feature Learning for Multi-View ClusteringIEEE Signal Processing Letters10.1109/LSP.2024.340860631(1595-1599)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3408606
Yang ZZhang HWei YWang ZNie FHu D(2024)Geometric-inspired graph-based Incomplete Multi-view ClusteringPattern Recognition10.1016/j.patcog.2023.110082147(110082)Online publication date: Mar-2024
https://doi.org/10.1016/j.patcog.2023.110082
Li YLiao H(2024)Diverse embeddings learning for multi-view clusteringPattern Analysis and Applications10.1007/s10044-024-01364-y28:1Online publication date: 6-Dec-2024
https://doi.org/10.1007/s10044-024-01364-y
Shi ZZhao H(2023)Deep Multi-View Clustering Based on Reconstructed Self-Expressive MatrixApplied Sciences10.3390/app1315879113:15(8791)Online publication date: 29-Jul-2023
https://doi.org/10.3390/app13158791
Ping KLi SWu CRen Z(2023)Architecture Alternative Deep Multi-View ClusteringIEEE Signal Processing Letters10.1109/LSP.2023.332765230(1547-1551)Online publication date: 2023
https://doi.org/10.1109/LSP.2023.3327652
Trosten DLøkse SJenssen RKampffmeyer M(2023)On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.02296(23976-23985)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.02296
Zhao GKou SYin XZhang GWang Y(2023)Self-supervised deep subspace clustering with entropy-normCluster Computing10.1007/s10586-023-04033-727:2(1611-1623)Online publication date: 1-Jun-2023
https://doi.org/10.1007/s10586-023-04033-7
Zhang YHuang QZhang BHe SDan TPeng HCai H(2022)Deep Multiview Clustering via Iteratively Self-Supervised Universal and Specific Space LearningIEEE Transactions on Cybernetics10.1109/TCYB.2021.308615352:11(11734-11746)Online publication date: Nov-2022
https://doi.org/10.1109/TCYB.2021.3086153
Bai RHuang RQin YChen Y(2022)Multi-view Document Clustering with Joint Contrastive LearningNatural Language Processing and Chinese Computing10.1007/978-3-031-17120-8_55(706-719)Online publication date: 24-Sep-2022
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