Correlation and Class Based Block Formation for Improved Structured Dictionary Learning

TL;DR

This paper introduces a novel correlation-based method for forming blocks in structured dictionary learning, improving reconstruction efficiency and classification performance by incorporating class information and reducing redundancy.

Contribution

It proposes a new correlation-based block formation approach and integrates class information to enhance dictionary learning for better reconstruction and classification.

Findings

Significant reduction in redundancy with correlation-based blocks

Improved classification accuracy in speaker verification tasks

Enhanced control over block size compared to SAC-based methods

Abstract

In recent years, the creation of block-structured dictionary has attracted a lot of interest. Learning such dictionaries involve two step process: block formation and dictionary update. Both these steps are important in producing an effective dictionary. The existing works mostly assume that the block structure is known a priori while learning the dictionary. For finding the unknown block structure given a dictionary commonly sparse agglomerative clustering (SAC) is used. It groups atoms based on their consistency in sparse coding with respect to the unstructured dictionary. This paper explores two innovations towards improving the reconstruction as well as the classification ability achieved with the block-structured dictionary. First, we propose a novel block structuring approach that makes use of the correlation among dictionary atoms. Unlike the SAC approach, which groups diverse atoms, in the proposed approach the blocks are formed by grouping the top most correlated atoms in the dictionary. The proposed block clustering approach is noted to yield significant reductions in redundancy as well as provides a direct control on the block size when compared with the existing SAC-based block structuring. Later, motivated by works using supervised \emph{a priori} known block structure, we also explore the incorporation of class information in the proposed block formation approach to further enhance the classification ability of the block dictionary. For assessment of the reconstruction ability with proposed innovations is done on synthetic data while the classification ability has been evaluated in large variability speaker verification task.