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论文类型：期刊论文

发表时间：2017-01-01

发表刊物：DIGITAL SIGNAL PROCESSING

收录刊物：SCIE、EI、Scopus

卷号：60

页面范围：113-121

ISSN号：1051-2004

关键字：Shape interaction matrix; Subspace clustering; Motion segmentation; Handwritten digit clustering

摘要：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. (C) 2016 Elsevier Inc. All rights reserved.