联合lp/l2,p范数极小化的序列子空间聚类算法

doi:10.16451/j.cnki.issn1003-6059.202003004

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Abstract To extract the spatio-temporal information in sequential data effectively, a sequential subspace clustering method via joint l_p/l_2,p-norms minimization is proposed. Firstly, a l_2,p-norm temporal graph is constructed to describe local similarity along the temporal direction by defining the sample-distance dependent weights. Secondly, since non-convex l_p-norm(0<p<1) minimization delivers better results than convex l₁-norm minimization does, and it removes more links between semantically-unrelated samples, l_p-norm is adopted to measure the sparsity of representation matrix. Finally, the linearized alternating direction method is employed to solve the optimization model. Experiments on video dataset, motion dataset, and face dataset confirm the effectiveness of the proposed method.

Key words： Subspace Clustering Sequential Data Sparse Coding Low-Rank Representation

Received: 08 November 2019

ZTFLH:

TP 391.41

Fund:Supported by National Natural Science Foundation of China(No.61863001, 61962003, 11661007, 61702244, 11761010), Natural Science Foundation of Jiangxi Province(No.20181BAB202021, 20192BAB205086), The Innovation Special Fund of Graduate of Jiangxi Province(No.YC2019-S393), Research Fund of Gannan Normal University(No.18zb04,YCX18B001)

Corresponding Authors: YI Yun, Ph.D., lecturer. His research interests include computer vision and video content analysis.

About author:: HU Wenyu, Ph.D., associate professor. His research interests include machine lear-ning and tensor computation. LI Shenghao, master student. His research interests include machine learning and computer vision. TU Zhihui, master student. His research interests include machine learning and computer vision.

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Cite this article:

HU Wenyu,LI Shenghao,TU Zhihui等. Sequential Subspace Clustering via Joint l_p/l_2,p-Norms Minimization[J]. , 2020, 33(3): 221-233.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202003004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I3/221

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