Locality Constrained Analysis Dictionary Learning via K-SVD Algorithm

TL;DR

This paper introduces a locality constrained analysis dictionary learning method using a synthesis K-SVD algorithm, which preserves local geometric structures in image data to improve classification performance.

Contribution

It proposes a novel locality constrained analysis dictionary learning model that incorporates graph regularization and is solved via synthesis K-SVD, enhancing discriminative capability.

Findings

Outperforms existing methods on image classification tasks.

Preserves local geometric structures in sparse representations.

Demonstrates improved classification accuracy.

Abstract

Recent years, analysis dictionary learning (ADL) and its applications for classification have been well developed, due to its flexible projective ability and low classification complexity. With the learned analysis dictionary, test samples can be transformed into a sparse subspace for classification efficiently. However, the underling locality of sample data has rarely been explored in analysis dictionary to enhance the discriminative capability of the classifier. In this paper, we propose a novel locality constrained analysis dictionary learning model with a synthesis K-SVD algorithm (SK-LADL). It considers the intrinsic geometric properties by imposing graph regularization to uncover the geometric structure for the image data. Through the learned analysis dictionary, we transform the image to a new and compact space where the manifold assumption can be further guaranteed. thus, the local geometrical structure of images can be preserved in sparse representation coefficients. Moreover, the SK-LADL model is iteratively solved by the synthesis K-SVD and gradient technique. Experimental results on image classification validate the performance superiority of our SK-LADL model.