Unsupervised Feature Selection with Feature Clustering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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We show that information on the inherent structure of multidimensional data derived from a factor analysis procedure is equivalent to information obtained by Fisher discriminant analysis techniques, provided certain conditions, usually required in the factor analysis model, are satisfied. The results advocate the use of a factor analysis approach when Fisher discriminant analysis is not applicable, such as, for instance, in clustering problems.