Active Dictionary Learning in Sparse Representation Based Classification

TL;DR

This paper introduces an active dictionary learning method for sparse representation classification that selects atoms based on classification and reconstruction errors, improving accuracy over traditional methods.

Contribution

The paper proposes a novel active dictionary learning approach that considers classification and reconstruction errors for atom selection, enhancing sparse representation classification.

Findings

The proposed method outperforms unsupervised and whole-data dictionary methods.

Experimental results show improved classification accuracy.

The method is effective on UCI and face datasets.

Abstract

Sparse representation, which uses dictionary atoms to reconstruct input vectors, has been studied intensively in recent years. A proper dictionary is a key for the success of sparse representation. In this paper, an active dictionary learning (ADL) method is introduced, in which classification error and reconstruction error are considered as the active learning criteria in selection of the atoms for dictionary construction. The learned dictionaries are caculated in sparse representation based classification (SRC). The classification accuracy and reconstruction error are used to evaluate the proposed dictionary learning method. The performance of the proposed dictionary learning method is compared with other methods, including unsupervised dictionary learning and whole-training-data dictionary. The experimental results based on the UCI data sets and face data set demonstrate the efficiency of the proposed method.