Statistical Learning Approaches for Discriminant Features Selection

Gilson A. GiraldiPaulo S. RodriguesEdson C. KitaniJoão R. satoCarlos E. Thomaz

Supervised statistical learning covers important modelslike Support Vector Machines (SVM) and Linear DiscriminantAnalysis (LDA). In this paper we describe theidea of using the discriminant weights given by SVM andLDA separating hyperplanes to select the most discriminantfeatures to separate sample groups. Our method,called here as Discriminant Feature Analysis (DFA), isnot restricted to any particular probability density functionand the number of meaningful discriminant featuresis not limited to the number of groups. To evaluate thediscriminant features selected, two case studies have beeninvestigated using face images and breast lesion data sets.In both case studies, our experimental results show thatthe DFA approach provides an intuitive interpretation ofthe differences between the groups, highlighting and reconstructingthe most important statistical changes betweenthe sample groups analyzed.

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