Abstract To solve the problem of multi-view clustering, a latent low-rank sparse multi-view subspace clustering(LLSMSC) algorithm is proposed. A latent space shared by all views is constructed to explore the complementary information of multi-view data. The global and local structure of multi-view data can be captured to attain promising clustering results by imposing low-rank constraint and sparse constraint on the implicit latent subspace representation simultaneously. An algorithm based on augmented Lagrangian multiplier with alternating direction minimization strategy is employed to solve the optimization problem. Experiments on six benchmark datasets verify the effectiveness and superiority of LLSMSC.
Fund:Supported by National Natural Science Foundation of China(No.61573273), State Key Laboratory of Rail Transit Engineering Informatization(FSDI)(No.SKLK19-01),
Corresponding Authors:
LI Chen, Ph.D., lecturer. Her research interests include multimedia technology and pattern recognition.
About author:: ZHANG Zhuohan, master student. His research interests include multi-view clus-tering.CAO Rongwei, master student. Her research interests include multi-view clus-tering.CHENG Shiqing, master student. Her research interests include machine learning and multi-view clustering.LI Weiwei, born in 1981, Ph.D., Lecturer. Her research interests include data mining.
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2020, Vol. 33 
