  We present a two-stage approach for learning dictionaries for object
classification tasks based on the principle of information maximization. The
proposed method seeks a dictionary that is compact, discriminative, and
generative. In the first stage, dictionary atoms are selected from an initial
dictionary by maximizing the mutual information measure on dictionary
compactness, discrimination and reconstruction. In the second stage, the
selected dictionary atoms are updated for improved reconstructive and
discriminative power using a simple gradient ascent algorithm on mutual
information. Experiments using real datasets demonstrate the effectiveness of
our approach for image classification tasks.
