Abstract：To extract the spatio-temporal information in sequential data effectively, a sequential subspace clustering method via joint lp/l2,p-norms minimization is proposed. Firstly, a l2,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 lp-norm(0<p<1) minimization delivers better results than convex l1-norm minimization does, and it removes more links between semantically-unrelated samples, lp-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.
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2020, Vol. 33 
