BI-sparsity pursuit for robust subspace recovery

Journal Article · Tue Sep 01 00:00:00 EDT 2015 · Conference: IEEE conference on Image Processing (ICIP)

DOI:https://doi.org/10.1109/ICIP.2015.7351462· OSTI ID:1438406

Bian, Xiao ^[1]; Krim, Hamid ^[1]

---

1. North Carolina State Univ., Raleigh, NC (United States)

Here, the success of sparse models in computer vision and machine learning in many real-world applications, may be attributed in large part, to the fact that many high dimensional data are distributed in a union of low dimensional subspaces. The underlying structure may, however, be adversely affected by sparse errors, thus inducing additional complexity in recovering it. In this paper, we propose a bi-sparse model as a framework to investigate and analyze this problem, and provide as a result , a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We additionally demonstrate the effectiveness of our method by experiments on real-world vision data.

• Research Organization: North Carolina State University, Raleigh, NC (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
• Grant/Contract Number: NA0002576
• OSTI ID: 1438406
• Journal Information: Conference: IEEE conference on Image Processing (ICIP), Journal Name: Conference: IEEE conference on Image Processing (ICIP)
• Country of Publication: United States
• Language: English

Related Subjects

47 OTHER INSTRUMENTATION
non-convex optimization
Sparse representation
signal recovery
video segmentation
face clustering