Improved linear discrimination using time-frequency dictionaries

Abstract
We consider linear discriminant analysis in the setting where the objects (signals/images) have many dimensions (samples/pixels) and there are relatively few training samples. We discuss ways that time frequency dictionaries can be used to adaptively select a small set of derived features which lead to improved misclassification rates.

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