Sparseness helps: Sparsity Augmented Collaborative Representation for Classification

TL;DR

This paper demonstrates that incorporating sparseness into collaborative representations enhances classification accuracy and efficiency, challenging the belief that collaboration alone suffices, and introduces an augmented method that outperforms existing approaches.

Contribution

The paper introduces a novel sparse augmentation to dense collaborative representations, improving classification accuracy and reducing computational cost.

Findings

Augmented sparse representations outperform dense methods in accuracy.

The proposed method reduces computational time compared to state-of-the-art.

Experiments on benchmark datasets validate the effectiveness of the approach.

Abstract

Many classification approaches first represent a test sample using the training samples of all the classes. This collaborative representation is then used to label the test sample. It was a common belief that sparseness of the representation is the key to success for this classification scheme. However, more recently, it has been claimed that it is the collaboration and not the sparseness that makes the scheme effective. This claim is attractive as it allows to relinquish the computationally expensive sparsity constraint over the representation. In this paper, we first extend the analysis supporting this claim and then show that sparseness explicitly contributes to improved classification, hence it should not be completely ignored for computational gains. Inspired by this result, we augment a dense collaborative representation with a sparse representation and propose an efficient classification method that capitalizes on the resulting representation. The augmented representation and the classification method work together meticulously to achieve higher accuracy and lower computational time compared to state-of-the-art collaborative representation based classification approaches. Experiments on benchmark face, object and action databases show the efficacy of our approach.