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For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that...
We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected `reference sample' before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high dimensional...
The Gaussian process (GP) approaches to classification synthesize Bayesian methods and kernel techniques, which are developed for the purpose of small sample analysis. Here we propose a GP model and investigate it for the facial expression recognition in the Japanese female facial expression dataset. By the strategy of leave-one-out cross validation, the accuracy of the GP classifiers reaches 93.43%...
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