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Function basis and approximation algorithm are two key elements in sparse representation. In this paper, some cardinal spline kernel basis and translation invariant fast decreasing kernel basis are presented, an iterative orthogonal matching pursuit algorithm (IOMP) is proposed, which is based on iterative update of hermitian inverse matrix. Experiments and comparisons on sparse representation of...
Dimensionality reduction is a significant problem in pattern recognition and thus arouses broad interest in the machine learning community. Different from the traditional linear dimensionality reduction methods, recently some nonlinear methods have been proposed in virtue of manifold learning. These methods can efficiently discover the low-dimensional nonlinear manifold in a high-dimensional data...
Fuzzy relational classifier (FRC) has been proven effective in both revealing the data structure and interpreting classification result. In FRC, fuzzy matrix R describing the relationship between the clusters and class labels plays an important role in its effective and robust classification. However, original FRC employs all the training samples undifferentiatedly to construct R, and thus leading...
Feature extraction using canonical correlation analysis (CCA) manipulates the pairwise samples from two information channels, say, X and Y, respectively, to realize the feature fusion in the context of multimodal recognition. To extract more discriminative features for recognition, a new supervised kernel-based learning method, namely kernelized discriminative CCA (KDCCA), is proposed. The superiority...
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