Recent years, analysis representation learning and its applications for classification have been well explored and applied, due to its flexible representation ability and low classification complexity. With a learned analysis dictionary, test samples can be transformed into a sparse subspace for classification efficiently. However, the underlying locality of sample data has rarely been explored and reliably married with analysis representation learning to enhance the discriminative capability of the classifier. In this paper, we propose a novel adaptive locality-sensitive analysis representation learning model for pattern classification (ALAR). It considers the intrinsic geometric properties by imposing adaptive weighted constrained graph regularization to uncover the geometric structure of 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 ALAR model is iteratively solved by the synthesis K-SVD and gradient technique. Experimental results on image classification validate the performance superiority of our ALAR model.
KEYWORDS: Associative arrays, Data modeling, Image classification, Performance modeling, Chemical species, Statistical modeling, Error control coding, Autoregressive models, Optimization (mathematics), Chemical elements
During the last decade, graph regularized dictionary learning (DL) models have obtained a lot of attention due to their flexible and discriminative ability in nonlinear pattern classification. However, the conventional graph-regularized methods construct a fixed affinity matrix for nearby samples in high-dimensional data space, which is vulnerable to noisy and redundant sample features. Furthermore, the discrimination of the graph regularized representation is not fully explored with the supervised classifier learning framework. To remedy these limitations, we propose an adaptive graph-regularized and label-embedded DL model for pattern classification. Especially, the affinity graph construction in low-dimensional representation space and the discriminative sparse representation is simultaneously learned in a unified framework for mutual promotion. More concretely, we iteratively update the sample similarity weight matrix in representation space to enhance the model robustness and further impose a supervised label-embedding term on sparse representation to enhancing its discriminative capability for classification. The dictionary orthonormal constraint is also considered to eliminate the redundant atoms and enhance the model discrimination. An efficient alternating direction solution with guaranteed convergence is proposed for the nonconvex and unsmooth model. Experimental results on five benchmark datasets verify the effectiveness of the proposed model.
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