Structure-Constrained Low-Rank Representation

doi:10.1109/TNNLS.2014.2306063

2014-12-01

137

PRE

DOI：10.1109/TNNLS.2014.2306063

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摘要

En 中文

Benefiting from its effectiveness in subspace segmentation, low-rank representation (LRR) and its variations have many applications in computer vision and pattern recognition, such as motion segmentation, image segmentation, saliency detection, and semisupervised learning. It is known that the standard LRR can only work well under the assumption that all the subspaces are independent. However, this assumption cannot be guaranteed in real-world problems. This paper addresses this problem and provides an extension of LRR, named structure-constrained LRR (SC-LRR), to analyze the structure of multiple disjoint subspaces, which is more general for real vision data. We prove that the relationship of multiple linear disjoint subspaces can be exactly revealed by SC-LRR, with a predefined weight matrix. As a nontrivial byproduct, we also illustrate that SC-LRR can be applied for semisupervised learning. The experimental results on different types of vision problems demonstrate the effectiveness of our proposed method.

Keyword:

Disjoint subspaces

low-rank representation (LRR)

semisupervised learning

subspace segmentation

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期刊

IEEE Transactions on Neural Networks and Learning Systems

IF:

8.9

论文数:

7.5K

被引数:

7.2W

机构

Dalian University of Technology

学者数:

6.0W

论文数: 4.4W

被引数: 5.5W

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