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Many Semi-supervised learning applications require a feature selection method to deal with the unlabeled samples. Traditional researches deal it either with the "filter-type" feature selection mechanism, which may not work well for classification tasks or "wrapper" mechanism, which need high computational cost. Here we proposed a new semi-supervised feature selection method based...
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...
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