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The problem of semi-supervised dimensionality reduction with kernels called KS2DR is considered for semi-supervised learning. In this setting, domain knowledge in the form of pair constraints is adopted to specify whether pairs of instances belong to the same class or not. KS2DR can project the samples data onto a set of `useful' features and preserve the structure of unlabeled samples data as well...
Many problems in intelligent data analysis involve some forms of dimensionality reduction. The paper discusses a new supervised dimensionality reduction method where samples are accompanied with class labels. We also show that it can be easily extended to the non-linear dimensionality reduction scenarios by the kernel tricks, and then we proposes an effective orthogonal feature subspace and correlation...
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