Multi-view subspace clustering with Kronecker-basis-representation-based tensor sparsity measure View Full Text


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Article Info

DATE

2021-10-11

AUTHORS

Gui-Fu Lu, Hua Li, Yong Wang, Ganyi Tang

ABSTRACT

Multi-view data are popular in many machine learning and computer vision applications. For example, in computer vision fields, one object can be described with images, text or videos. Recently, multi-view subspace clustering approaches, which can make use of the complementary information among different views to improve the performance of clustering, have attracted much attention. In this paper, we propose a novel multi-view subspace clustering method with Kronecker-basis-representation-based tensor sparsity measure (MSC-KBR) to address multi-view subspace clustering problem. In the MSC-KBR model, we first construct a tensor based on the subspace representation matrices of different views, and, then the high-order correlations underlying different views can be explored. We also adopt a novel Kronecker-basis-representation-based tensor sparsity measure (KBR) to the constructed tensor to reduce the redundancy of the learned subspace representations and improve the accuracy of clustering. Different from the traditional unfolding-based tensor norm, KBR can encode both sparsity insights delivered by Tucker and CANDECOMP/PARAFAC decompositions for a general tensor. By using the augmented Lagrangian method, an efficient algorithm is presented to solve the optimization problem of the MSC-KBR model. The experimental results on some datasets show that the proposed MSC-KBR model outperforms many state-of-the-art multi-view clustering approaches. More... »

PAGES

123

References to SciGraph publications

  • 2008-10-15. Enhancing Sparsity by Reweighted ℓ1 Minimization in JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS
  • 2006-01-01. A Survey of Clustering Data Mining Techniques in GROUPING MULTIDIMENSIONAL DATA
  • 1966-09. Some mathematical notes on three-mode factor analysis in PSYCHOMETRIKA
  • 2018-04-06. On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2019-11-15. Uncertainty-optimized deep learning model for small-scale person re-identification in SCIENCE CHINA INFORMATION SCIENCES
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    http://scigraph.springernature.com/pub.10.1007/s00138-021-01247-w

    DOI

    http://dx.doi.org/10.1007/s00138-021-01247-w

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