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Improved Singular Value Decomposition for Supervised Learning in a High Dimensional Dataset

Singular Value Decomposition (SVD) is a useful technique for dimensionality reduction with a controlled loss of information. This paper makes the very simple but worth-while observation that many attributes that contain no information about the class label, may thus be selected erroneously for a supervised learning task. We propose to first use a very tolerant filter to select on a univariate basis which attributes to include in the subsequent SVD. The features, “the latent variables”, extracted from relevant descriptors allow to build a better classifier with a significant improvement of the generalization error rate and less cpu time. We show the efficiency of this combination of feature selection and construction approaches on a protein classification context.