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In this paper, we introduce a novel semi-random subspace sampling for classification (for short, denoted by FS_RS). In this method, a ranking feature list is obtained by using feature selection first, and then the more important N0 features in the front of the ranking feature list are chosen, and N1 features is randomly selected from the remaining features in the ranking feature list. Along this sampling...
Classical Linear Discriminant Analysis (LDA) is usually suffers from the small sample size (SSS) problem when dealing with the high dimensional face data. Many methods have been proposed for solving this problem such as Fisherface and Null Space LDA (N-LDA), but these methods are overfitted to the training set and inevitably lose some useful discriminative information in many cases. To effectively...
Feature selection is an important problem for pattern classifier systems. As compared to unsupervised feature selection methods, supervised feature selection approaches have better performance when the given training samples with supervised information are sufficient. However, in reality, usually only a few labeled data are obtained, since obtaining class labels is expensive but many unlabeled data...
Fuzzy relational classifier (FRC) is the recently proposed two-step nonlinear classifiers, which effectively integrates the formed clusters and the given classes. However, FRC can not copy with the influence of those irrelevant or redundant features. To effectively filter out those irrelevant features and preserve the internal structure hidden in the given data, in this paper, a simultaneous clustering...
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