Abstract
We propose a novel structured discriminative blockdiagonal dictionary learning method, referred to as
scalable Locality-Constrained Projective Dictionary
Learning (LC-PDL), for efficient representation and
classification. To improve the scalability by saving
both training and testing time, our LC-PDL aims at
learning a structured discriminative dictionary and a
block-diagonal representation without using costly
l0/l1-norm. Besides, it avoids extra time-consuming
sparse reconstruction process with the well-trained
dictionary for new sample as many existing models.
More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary
over each class. To enhance the performance, we
incorporate a locality constraint of atoms into the
DL procedures to keep local information and obtain
the codes of samples over each class separately. A
block-diagonal discriminative approximation term
is also derived to learn a discriminative projection to
bridge data with their codes by extracting the special
block-diagonal features from data, which can ensure
the approximate coefficients to associate with its
label information clearly. Then, a robust multiclass
classifier is trained over extracted block-diagonal
codes for accurate label predictions. Experimental
results verify the effectiveness of our algorithm