Learning Semantic Patterns with Discriminant Localized Binary Projections

In this paper, we present a novel approach to learning semantic localized patterns with binary projections in a supervised manner. The pursuit of these binary projections is reformulated into a problem of feature clustering, which optimizes the separability of different classes by taking the members within each cluster as the nonzero entries of a projection vector. An efficient greedy procedure is proposed to incrementally combine the sub-clusters by ensuring the cardinality constraints of the projections and the increase of the objective function. Compared with other algorithms for sparse representations, our proposed algorithm, referred to as Discriminant Localized Binary Projections (dlb), has the following characteristics: 1) dlb is supervised, hence is much more effective than other unsupervised sparse algorithms like Non-negative Matrix Factorization (NMF) in terms of classification power; 2) similar to NMF, dlb can derive spatially localized sparse bases; furthermore, the sparsity of dlb is controllable, and an interesting result is that the bases have explicit semantics in human perception, like eyes and mouth; and 3) classification with dlb is extremely efficient, and only addition operations are required for dimensionality reduction. Extensive experimental results show significant improvements of dlb in sparsity and face recognition accuracy in comparison to the state-of-the-art algorithms for dimensionality reduction and sparse representations.