Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints

TL;DR

This paper introduces JPDL-LR, a novel method combining joint projection and dictionary learning with low-rank regularization and graph constraints to improve classification robustness on noisy data.

Contribution

It proposes a new joint learning framework that integrates low-rank regularization and graph constraints to enhance noise robustness and discriminative power.

Findings

Outperforms existing methods on benchmark datasets.

Effective in noisy and heavily corrupted data scenarios.

Improves classification accuracy with robust feature representation.

Abstract

In this paper, we aim at learning simultaneously a discriminative dictionary and a robust projection matrix from noisy data. The joint learning, makes the learned projection and dictionary a better fit for each other, so a more accurate classification can be obtained. However, current prevailing joint dimensionality reduction and dictionary learning methods, would fail when the training samples are noisy or heavily corrupted. To address this issue, we propose a joint projection and dictionary learning using low-rank regularization and graph constraints (JPDL-LR). Specifically, the discrimination of the dictionary is achieved by imposing Fisher criterion on the coding coefficients. In addition, our method explicitly encodes the local structure of data by incorporating a graph regularization term, that further improves the discriminative ability of the projection matrix. Inspired by recent advances of low-rank representation for removing outliers and noise, we enforce a low-rank constraint on sub-dictionaries of all classes to make them more compact and robust to noise. Experimental results on several benchmark datasets verify the effectiveness and robustness of our method for both dimensionality reduction and image classification, especially when the data contains considerable noise or variations.