Structure-Aware Classification using Supervised Dictionary Learning

TL;DR

This paper introduces a supervised dictionary learning method that incorporates graph-based regularization to preserve local data geometry, enhancing classification accuracy across various datasets.

Contribution

It presents a novel graph-regularized supervised dictionary learning algorithm that adapts to data manifold structures for improved classification performance.

Findings

Outperforms existing dictionary-based methods on multiple datasets

Effectively preserves local data geometry during learning

Improves classification accuracy in both single-label and multi-label tasks

Abstract

In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the data points. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. The combination of these two terms promotes the discriminative power of the learned sparse representations and leads to improved classification accuracy. The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.