  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.
