Letters: Enhancing sparsity via ℓp (0

Authors:
Song Guo;Zhan Wang;Qiuqi Ruan
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
Venue:
Neurocomputing
Year:
2013

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Abstract

Sparse representation has received an increasing amount of interest in recent years. By representing the testing image as a sparse linear combination of the training samples, sparse representation based classification (SRC) has been successfully applied in face recognition. In SRC, the @?^1 minimization instead of the @?^0 minimization is used to seek for the sparse solution for its computational convenience and efficiency. However, @?^1 minimization does not always yield sufficiently sparse solution in many practical applications. In this paper, we propose a novel SRC method, namely the @?^p (0