Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

TL;DR

This paper introduces a robust dictionary learning method in a factorized compressed space that enhances data representation by combining low-rank and sparse features, improving robustness to noise and outliers.

Contribution

The paper proposes the J-RFDL model, integrating robust factorization and projective dictionary learning with joint low-rank and sparse constraints, and extends it for discriminative classification tasks.

Findings

Outperforms state-of-the-art methods on public datasets

Enhances robustness to noise and outliers in data

Improves reconstruction accuracy and discriminative ability

Abstract

In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) model is presented. The setting of J-RFDL aims at improving the data representations by enhancing the robustness to outliers and noise in data, encoding the reconstruction error more accurately and obtaining hybrid salient coefficients with accurate reconstruction ability. Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient. To make the encoding process robust to noise in data, J-RFDL clearly uses sparse L2, 1-norm that can potentially minimize the factorization and reconstruction errors jointly by forcing rows of the reconstruction errors to be zeros. To deliver salient coefficients with good structures to reconstruct given data well, J-RFDL imposes the joint low-rank and sparse constraints on the embedded coefficients with a synthesis dictionary. Based on the hybrid salient coefficients, we also extend J-RFDL for the joint classification and propose a discriminative J-RFDL model, which can improve the discriminating abilities of learnt coeffi-cients by minimizing the classification error jointly. Extensive experiments on public datasets demonstrate that our formulations can deliver superior performance over other state-of-the-art methods.