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In this paper, we propose a recursive soft margin (RSM) subspace learning framework for dimension reduction of high-dimensional data, which has strong recognition ability. RSM is motivated by the soft margin criterion of support vector machines (SVMs), which allows some training samples to be misclassified for a certain cost to achieve higher recognition results. Instead of maximizing the sum of squares...
Orthogonalized variant of Linear Discriminant Analysisis (LDA) is an effective statistical learning tool for dimension reduction. However, existing orthogonalized LDA algorithms suffer from various drawbacks, including the requirement for expensive computing time. This paper develops an efficient algorithm for dimension reduction, referred to as Fast Orthogonal Linear Discriminant Analysis (FOLDA),...
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