Accession Number:
AD1023874
Title:
Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery
Descriptive Note:
Technical Report,27 Sep 2015,30 Sep 2015
Corporate Author:
North Carolina State University Raleigh United States
Personal Author(s):
Report Date:
2016-09-27
Pagination or Media Count:
21.0
Abstract:
The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional datais distributed in a union of low dimensional subspaces in many real-world applications. The underlying structuremay, however, be adversely affected by sparse errors. In this paper, we propose a bi-sparse model as a framework toanalyze this problem, and provide a novel algorithm to recover the union of subspaces in presence of sparsecorruptions. We further show the effectiveness of our method in a number of applications using real-world visiondata.
Descriptors:
Subject Categories:
- Cybernetics