Block and Group Regularized Sparse Modeling for Dictionary Learning

Yu-Tseh Chi, Mohsen Ali, Ajit Rajwade, Jeffrey Ho

Abstract:

This paper proposes a dictionary learning framework

that combines the proposed block/group (BGSC) or recon-
structed block/group (R-BGSC) sparse coding schemes with

the novel Intra-block Coherence Suppression Dictionary

Learning (ICS-DL) algorithm. An important and distin-
guishing feature of the proposed framework is that all dic-
tionary blocks are trained simultaneously with respect to

each data group while the intra-block coherence being ex-
plicitly minimized as an important objective. We provide

both empirical evidence and heuristic support for this fea-
ture that can be considered as a direct consequence of in-
corporating both the group structure for the input data and

the block structure for the dictionary in the learning pro-
cess. The optimization problems for both the dictionary

learning and sparse coding can be solved efficiently using
block-gradient descent, and the details of the optimization

algorithms are presented. We evaluate the proposed meth-
ods using well-known datasets, and favorable comparisons

with state-of-the-art dictionary learning methods demon-
strate the viability and validity of the proposed framework.