Related papers: Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation

Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation

URL: http://arxiv.org/abs/2004.14705v2
Date: Sat, 1 Aug 2020 13:35:01 GMT
Title: Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
Authors: Yuheng Jia, Hui Liu, Junhui Hou, Sam Kwong, Qingfu Zhang
Abstract summary: This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. We design a novel structured tensor low-rank norm tailored to MVSC. We show that the proposed method outperforms state-of-the-art methods to a significant extent.
Score: 105.33409035876691
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike the existing methods that all adopt an off-the-shelf tensor low-rank norm without considering the special characteristics of the tensor in MVSC, we design a novel structured tensor low-rank norm tailored to MVSC. Specifically, we explicitly impose a symmetric low-rank constraint and a structured sparse low-rank constraint on the frontal and horizontal slices of the tensor to characterize the intra-view and inter-view relationships, respectively. Moreover, the two constraints could be jointly optimized to achieve mutual refinement. On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively. Extensive experimental results on five benchmark datasets show that the proposed method outperforms state-of-the-art methods to a significant extent. Impressively, our method is able to produce perfect clustering. In addition, the parameters of our method can be easily tuned, and the proposed model is robust to different datasets, demonstrating its potential in practice.

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