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We introduce a technique for extending the classical method of Linear Discriminant Analysis to data sets where the predictor variables are curves or functions. This procedure, which we call functional...
In high-dimensional classification problems, one is often interested in ?? finding a few important discriminant directions in order to reduce the dimensionality.Fisher’s linear discriminant analysis(L...
In this paper, we introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “near...
Sparse Discriminant Analysis     Sparse  Discriminant Analysis       2015/8/21
We consider the problem of performing interpretable classification in the high-dimensional setting, in which the number of features is very large and the number of observations is limited. This settin...
Abstract: This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\O$ and...
Most classification methods provide either a prediction of group membership or an assessment of class membership probability. In the case of two-group classification the predicted probability can be d...

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