Accession Number:

ADA571144

Title:

Kernel Dictionary Learning

Descriptive Note:

Conference paper

Corporate Author:

MARYLAND UNIV COLLEGE PARK

Report Date:

2012-03-01

Pagination or Media Count:

6.0

Abstract:

In this paper, we present dictionary learning methods for sparse and redundant signal representations in high dimensional feature space. Using the kernel method, we describe how the well-known dictionary learning approaches such as the method of optimal directions and K-SVD can be made nonlinear. We analyze these constructions and demonstrate their improved performance through several experiments on classification problems. It is shown that nonlinear dictionary learning approaches can provide better discrimination compared to their linear counterparts and kernel PCA, especially when the data is corrupted by noise.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE