2018 journal article

Bi-sparsity pursuit: A paradigm for robust subspace recovery

Signal Processing, 152, 148–159.

By: X. Bian  n, A. Panahi n & H. Krim n

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: Signal recovery; Sparse learning; Subspace modeling
Source: Crossref
Added: February 24, 2020

The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world applications. The underlying structure may, however, be adversely affected by sparse errors. In this paper, we propose a bi-sparse model as a framework to analyze this problem, and provide a novel algorithm to recover the union of subspaces in the presence of sparse corruptions. We further show the effectiveness of our method by experiments on real-world vision data.