Paper Info
Title
Geometric Conditions for Subspace-Sparse Recovery
Abstract
Given a dictionary Π and a signal ξ = Πx generated by a few linearly independent columns of Π, classical sparse recovery theory deals with the problem of uniquely recovering the sparse representation x of ξ. In this work, we consider the more general case where ξ lies in a low-dimensional subspace spanned by a few columns of Π, which are possibly linearly dependent. In this case, x may not unique, and the goal is to recover any subset of the columns of Π that spans the subspace containing ξ. We call such a representation x subspace-sparse. We study conditions under which existing pursuit methods recover a subspace-sparse representation. Such conditions reveal important geometric insights and have implications for the theory of classical sparse recovery as well as subspace clustering.
Year
Venue
Field
2015
International Conference on Machine Learning
Linear independence,Subspace clustering,Subspace topology,Computer science,Sparse approximation,Artificial intelligence,Machine learning
DocType
Citations 
PageRank 
Conference
10
0.47
References 
Authors
9
2
Name
Order
Citations
PageRank
Chong You11328.07
rene victor valqui vidal25331260.14