Abstract In the existing subspace clustering algorithms, it is assumed that the data is derived from a union of multiple linear subspace, and these algorithms cannot deal with problems of nonlinear and time warping in time series clustering. To overcome these issues, elastic kernel low rank representation subspace clustering(EKLRR) and elastic kernel least squares regression subspace clustering(EKLSR) are proposed by introducing kernel tricks and elastic distance, and they are called elastic kernel subspace clustering(EKSC). Moreover, the grouping effect of EKLSR and the convergence of EKLRR are proved theoretically. The experimental results on five UCR datasets show the effectiveness of the proposed algorithms.
About author:: (ZHANG Pengtao, born in 1991, master student. His research interests include data mining, pattern recognition, and machine learning.) (CHEN Xiaoyun(Corresponding author), born in 1970, Ph.D., professor. Her research interests include data mining, pattern recognition, and machine learning.)
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2017, Vol. 30 
