Consider the problem of classifying a number of objects into one of several groups or classes based on a set of characteristics X = (X1 , X2 , . . . , Xp ). This problem has been extensively studied under the general subject of discriminant analysis in the statistical literature, or supervised pattern recognition in the machine learning field. Recently, dimension reduction methods, such as SIR and SAVE, have been used for classification purposes. In this paper we propose a regularized version of the SIR method which is able to gain information from the structure of both class means and class variances.
Regularized sliced inverse regression with applications in classification
SCRUCCA, Luca
2005
Abstract
Consider the problem of classifying a number of objects into one of several groups or classes based on a set of characteristics X = (X1 , X2 , . . . , Xp ). This problem has been extensively studied under the general subject of discriminant analysis in the statistical literature, or supervised pattern recognition in the machine learning field. Recently, dimension reduction methods, such as SIR and SAVE, have been used for classification purposes. In this paper we propose a regularized version of the SIR method which is able to gain information from the structure of both class means and class variances.File in questo prodotto:
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