  Recent years, analysis dictionary learning (ADL) and its applications for
classification have been well developed, due to its flexible projective ability
and low classification complexity. With the learned analysis dictionary, test
samples can be transformed into a sparse subspace for classification
efficiently. However, the underling locality of sample data has rarely been
explored in analysis dictionary to enhance the discriminative capability of the
classifier. In this paper, we propose a novel locality constrained analysis
dictionary learning model with a synthesis K-SVD algorithm (SK-LADL). It
considers the intrinsic geometric properties by imposing graph regularization
to uncover the geometric structure for the image data. Through the learned
analysis dictionary, we transform the image to a new and compact space where
the manifold assumption can be further guaranteed. thus, the local geometrical
structure of images can be preserved in sparse representation coefficients.
Moreover, the SK-LADL model is iteratively solved by the synthesis K-SVD and
gradient technique. Experimental results on image classification validate the
performance superiority of our SK-LADL model.
