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Most existing feature selection methods focus on ranking individual features based on a utility criterion, which neglecting the correlations among features. To overcome this problem, we develop a novel feature selection technique using the spectral data transformation and by using l1-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique...
Semi-supervised learning is important when labeled data are scarce. In this paper, we develop a novel semi-supervised spectral feature selection technique using label regression and by using l\-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for semi-supervised feature selection. In the first step, we use label propagation and label...
Most existing feature selection methods focus on ranking features based on an information criterion to select the best K features. However, several authors have found that the optimal feature combinations do not give the best classification performance [8],[7]. The reason for this is that although an individual feature may have limited relevance to a particular class, when taken in combination with...
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