Joint Dual-Structural Constrained and Non-negative Analysis Representation Learning for Pattern Classification

Abstract

ABSTRACT In recent years, analysis dictionary learning (ADL) model has attracted much attention from researchers, owing to its scalability and efficiency in representation-based classification. Despite the supervised label information embedding, the classification performance of analysis representation suffers from the redundant and noisy samples in real-world datasets. In this paper, we propose a joint Dual-Structural constrained and Non-negative Analysis Representation (DSNAR) learning model. First, the supervised latent structural transformation term is considered implicitly to generate a roughly block diagonal representation for intra-class samples. However, this discriminative structure is fragile and weak in the presence of noisy and redundant samples. To highlight both intra-class similarity and inter-class separation for class-oriented representation, we then explicitly incorporate an off-block suppressing term on the ADL model, together with a non-negative representation constraint, to achieve a well-structured and meaningful interpretation of the contributions from all class-oriented atoms. Moreover, a robust classification scheme in latent space is proposed to avoid accidental incorrect predictions with noisy information. Finally, the DSNAR model is alternatively solved by the K-SVD method, iterative re-weighted method and gradient method efficiently. Extensive classification results on five benchmark datasets validate the performance superiority of our DSNAR model compared to other state-of-the-art DL models.