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Sparse linear discriminant analysis via penalized optimal scoring is asuccessful tool for classification in high-dimensional settings. While thevariable selection consistency of sparse optimal scoring has been established,the corresponding prediction and estimation consistency results have beenlacking. We bridge this gap by providing probabilistic bounds on out-of-sampleprediction error and estimation error of multi-class penalized optimal scoringallowing for diverging number of classes.

Prediction and estimation consistency of sparse multi-class penalized optimal scoring