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

Correlation and Class Based Block Formation for Improved Structured Dictionary Learning