Subspace segmentation by dense block and sparse representation.

Published

Journal Article

Subspace segmentation is a fundamental topic in computer vision and machine learning. However, the success of many popular methods is about independent subspace segmentation instead of the more flexible and realistic disjoint subspace segmentation. Focusing on the disjoint subspaces, we provide theoretical and empirical evidence of inferior performance for popular algorithms such as LRR. To solve these problems, we propose a novel dense block and sparse representation (DBSR) for subspace segmentation and provide related theoretical results. DBSR minimizes a combination of the 1,1-norm and maximum singular value of the representation matrix, leading to a combination of dense block and sparsity. We provide experimental results for synthetic and benchmark data showing that our method can outperform the state-of-the-art.

Full Text

Duke Authors

Cited Authors

  • Tang, K; Dunson, DB; Su, Z; Liu, R; Zhang, J; Dong, J

Published Date

  • March 2016

Published In

Volume / Issue

  • 75 /

Start / End Page

  • 66 - 76

PubMed ID

  • 26720247

Pubmed Central ID

  • 26720247

Electronic International Standard Serial Number (EISSN)

  • 1879-2782

International Standard Serial Number (ISSN)

  • 0893-6080

Digital Object Identifier (DOI)

  • 10.1016/j.neunet.2015.11.011

Language

  • eng