Locality-constrained nonnegative robust shape interaction subspace clustering and its applications

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• 作者：Bo Li^a ; ^{bolimath@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chunyuan Lu^aAuthor Vitae ; Zhijie Wen^bAuthor Vitae ; Chengcai Leng^aAuthor Vitae ; Xiuping Liu^cAuthor Vitae
• 关键词：Shape interaction matrix ; Subspace clustering ; Motion segmentation ; Handwritten digit clustering
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：60
• 期：Complete
• 页码：113-121
• 全文大小：2610 K

文摘

In this paper, we present a locality-constrained nonnegative robust shape interaction (LNRSI) subspace clustering method. LNRSI integrates the local manifold structure of data into the robust shape interaction (RSI) in a unified formulation, which guarantees the locality and the low-rank property of the optimal affinity graph. Compared with traditional low-rank representation (LRR) learning method, LNRSI can not only pursuit the global structure of data space by low-rank regularization, but also keep the locality manifold, which leads to a sparse and low-rank affinity graph. Due to the clear block-diagonal effect of the affinity graph, LNRSI is robust to noise and occlusions, and achieves a higher rate of correct clustering. The theoretical analysis of the clustering effect is also discussed. An efficient solution based on linearized alternating direction method with adaptive penalty (LADMAP) is built for our method. Finally, we evaluate the performance of LNRSI on both synthetic data and real computer vision tasks, i.e., motion segmentation and handwritten digit clustering. The experimental results show that our LNRSI outperforms several state-of-the-art algorithms.