Latent Multi-view Subspace Clustering

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Latent Multi-view Subspace Clustering
Title:
Latent Multi-view Subspace Clustering
Other Titles:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Keywords:
Publication Date:
21 July 2017
Citation:
C. Zhang, Q. Hu, H. Fu, P. Zhu and X. Cao, "Latent Multi-view Subspace Clustering," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 4333-4341. doi: 10.1109/CVPR.2017.461
Abstract:
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and simultaneously performs data reconstruction based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict data themselves more comprehensively than each single view individually, accordingly makes subspace representation more accurate and robust as well. The proposed method is intuitive and can be optimized efficiently by using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1063-6919
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