Consistent and Specific Multi-View Subspace Clustering

By Shirui Luo, Changqing Zhang, Wei Zhang, Xiaochun Cao. AAAI Conference on Artificial Intelligence, 2018

439 citations recorded by the source corpus.

Multi-view clustering has attracted intensive attention due to the effectiveness of exploiting multiple views of data. However, most existing multi-view clustering methods only aim to explore the consistency or enhance the diversity of different views. In this paper, we propose a novel multi-view subspace clustering method (CSMSC), where consistency and specificity are jointly exploited for subspace representation learning. We formulate the multi-view self-representation property using a shared consistent representation and a set of specific representations, which better fits the real-world datasets. Specifically, consistency models the common properties among all views, while specificity captures the inherent difference in each view. In addition, to optimize the non-convex problem, we introduce a convex relaxation and develop an alternating optimization algorithm to recover the corresponding data representations. Experimental evaluations on four benchmark datasets demonstrate that the proposed approach achieves better performance over several state-of-the-arts.

Subfields: Computer Vision and Pattern Recognition, Urban Studies

View Paper: https://www.semanticscholar.org/paper/ccb96345a77b2d42012c891ffca221854b14f36a

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In Computer Vision and Pattern Recognition

1. En Zhu
2. Wenxuan Tu
3. Wangtao Zhou
4. Zhao Kang
5. Zenglin Xu
6. Zhitong Zhao
7. Xinwang Liu
8. Pei Zhang

Citations in the corpus

• Scalable Multi-view Subspace Clustering with Unified Anchors (2021, background): “[20] propose to learn the subspace representation with consistency and specificity jointly.”
• Large-scale Multi-view Subspace Clustering in Linear Time (2019, background): “…and all graphs are assumed to share a unique cluster matrix; (Cao et al. 2015) applies a diversity term to explore the complementarity information; (Luo et al. 2018) explores both consistency and specificity of different views; (Wang et al. 2019; Zhang et al. 2017) perform MVSC in latent…”
• Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, neutral): “subdivided into three subgroups, including subspace methods [6,18,24], matrix factorization methods [45,53,56], and graph methods [28, 55, 60].”