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- Title
Rank-sparsity balanced representation for subspace clustering.
- Authors
Xia, Yuqing; Zhang, Zhenyue
- Abstract
Subspace learning has many applications such as motion segmentation and image recognition. The existing algorithms based on self-expressiveness of samples for subspace learning may suffer from the unsuitable balance between the rank and sparsity of the expressive matrix. In this paper, a new model is proposed that can balance the rank and sparsity well. This model adopts the log-determinant function to control the rank of solution. Meanwhile, the diagonals are penalized, rather than the strict zero-restriction on diagonals. This strategy makes the rank-sparsity balance more tunable. We furthermore give a new graph construction from the low-rank and sparse solution, which absorbs the advantages of the graph constructions in the sparse subspace clustering and the low-rank representation for further clustering. Numerical experiments show that the new method, named as RSBR, can significantly increase the accuracy of subspace clustering on the real-world data sets that we tested.
- Subjects
SUBSPACES (Mathematics); CLUSTERING of particles; LOW-rank matrices; IMAGE recognition (Computer vision); SELF-expression
- Publication
Machine Vision & Applications, 2018, Vol 29, Issue 6, p979
- ISSN
0932-8092
- Publication type
Article
- DOI
10.1007/s00138-018-0918-y