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In this paper, the 3D head model classification problem is addressed by use of a newly developed subspace analysis method: kernel clustering-based discriminant analysis or KCDA as an abbreviation. This method works by first mapping the original data into another high-dimensional space, and then performing clustering-based discriminant analysis in the feature space. The main idea of clustering-based...
In this paper, a kernelized version of nonparametric discriminant analysis is proposed that we name KNDA. The main idea is to first map the original data into another high-dimensional space, and then to perform nonparametric discriminant analysis in the high dimensional space. Nonparametric discriminant analysis can relax the Gaussian assumption required for the classical linear discriminant analysis,...
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