Multiple kernel learning for sparse representation-based classification

Ashish Shrivastava, Vishal M. Patel, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms.

Original languageEnglish (US)
Article number6815769
Pages (from-to)3013-3024
Number of pages12
JournalIEEE Transactions on Image Processing
Volume23
Issue number7
DOIs
StatePublished - Jul 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • Sparse representation-based classification
  • kernel sparse representation
  • multiple kernel learning
  • object recognition

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