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A novel Hierarchical Structured Dictionary Learning (HSDL) algorithm is proposed in this paper. It aims to learn classs-pecific dictionaries for all classes simultaneously in a hierarchical structure. A discriminative term based on Fisher discrimination criterion is jointly considered for both the classs-pecific dictionaries in the lower level and the shared dictionaries in the upper level to enhance...
Feature learning is an intensively studied research topic in image classification. Although existing methods like sparse coding, locality-constrained linear coding, fisher vector encoding, etc., have shown their effectiveness in image representation, most of them overlook a phenomenon called thesmall sample size problem, where the number of training samples is relatively smaller than the dimensionality...
An important, yet under-explored, problem in pattern recognition concerns learning from data labeled at varying levels of specificity. The majority of existing machine learning methods are based on the inductive learning paradigm, where a labeled training set (one label per training example) trains a classifier which is markedly different from the human learning experience, where any one object can...
Unsupervised discriminant projection (UDP) has a good effect on face recognition problem, but it has not made full use of the training samples' class information that is useful for classification. Linear discrimination analysis (LDA) is a classical face recognition method. It is effective for classification, but it can not discover the samples' nonlinear structure. This paper develops a manifold-based...
A new microcalcification clusters (MCs) detection method in mammograms is proposed in this paper, which is based on a new ensemble learning method. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimensional image features are extracted by the feature extractor and fed to the ensemble decision model...
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