A subspace clustering algorithm based on simultaneously sparse and low-rank representation

作者：Liu, Xiaolan^*; Yi, Miao; Han, Le; Deng, Xue

来源：Journal of Intelligent and Fuzzy Systems, 2017, 33(1): 621-633.

DOI：10.3233/JIFS-16771

• Subspace clustering
• sparse representation
• low-rank representation
• motion segmentation
• face clustering

Sparse subspace clustering (SSC) and low-rank representation (LRR) are the state-of-the-art methods for subspace clustering, which force the representation matrix to be sparse and low-rank, respectively. Considering that sparsity and low rankness are of complementarity and the clean data matrix can be expressed as the combination of itself, in this paper, a new algorithm, named sparse and low-rank subspace clustering (SSC LRR for short), was proposed. SSC LRR decomposes the data matrix as the sum of a self-express clean dictionary and an error matrix, and forces the linear representation coefficient matrix to be simultaneously sparse and low-rank. An alternating direction method of multipliers(ADMM) based algorithm was developed to solve the SSC LRR problem. Experimental results on synthetic data, motion segmentation and face clustering tasks demonstrate the effectiveness of our approach.

• 出版日期2017
• 单位华南理工大学; 宜春学院