Classification of unions of subspaces with sparse representations

We propose a preliminary investigation on the benefits and limitations of classifiers based on sparse representations. We specifically focus on the union of subspaces data model and examine binary classifiers built on a sparse non linear mapping (in a redundant dictionary) followed by a linear classifier. We study two common sparse non linear mappings (namely \ell_0 and \ell_1) and show that, in both cases, there exists a finite dictionary such that the classifier discriminates the two classes correctly. This result paves the way towards a better understanding of the increasingly popular classifiers based on sparse representation


Presented at:
Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 3-6, 2013
Year:
2013
Note:
Invited paper
Laboratories:




 Record created 2014-01-10, last modified 2018-09-13

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