  In this paper, we propose a non-negative representation based discriminative
dictionary learning algorithm (NRDL) for multicategory face classification. In
contrast to traditional dictionary learning methods, NRDL investigates the use
of non-negative representation (NR), which contributes to learning
discriminative dictionary atoms. In order to make the learned dictionary more
suitable for classification, NRDL seamlessly incorporates nonnegative
representation constraint, discriminative dictionary learning and linear
classifier training into a unified model. Specifically, NRDL introduces a
positive constraint on representation matrix to find distinct atoms from
heterogeneous training samples, which results in sparse and discriminative
representation. Moreover, a discriminative dictionary encouraging function is
proposed to enhance the uniqueness of class-specific sub-dictionaries.
Meanwhile, an inter-class incoherence constraint and a compact graph based
regularization term are constructed to respectively improve the
discriminability of learned classifier. Experimental results on several
benchmark face data sets verify the advantages of our NRDL algorithm over the
state-of-the-art dictionary learning methods.
