Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

TL;DR

This paper introduces TP-DPL, a novel dictionary learning framework that enhances classification by integrating twin-incoherence constraints, adaptive neighborhood preservation, and efficient reconstruction, achieving state-of-the-art results.

Contribution

The paper proposes a new twin-projective latent dictionary learning model, TP-DPL, that unifies feature extraction, representation, and classification with improved accuracy and efficiency.

Findings

Achieves state-of-the-art classification performance on public datasets.

Effectively maintains intra-class compactness and inter-class separation.

Reduces computational cost by avoiding l0/l1-norm regularization.

Abstract

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. As a result, TP-DPL unifies the salient feature extraction, representation and classification. The twin-incoherence constraint on codes and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve the local neighborhood of the coefficients and salient features within each class explicitly. For efficiency, TP-DPL uses Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.