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Feature learning forms the cornerstone for tackling challenging learningproblems in domains such as speech, computer vision and natural languageprocessing. In this paper, we consider a novel class of matrix andtensor-valued features, which can be pre-trained using unlabeled samples. Wepresent efficient algorithms for extracting discriminative information, giventhese pre-trained features and labeled samples for any related task. Our classof features are based on higher-order score functions, which capture localvariations in the probability density function of the input. We establish atheoretical framework to characterize the nature of discriminative informationthat can be extracted from score-function features, when used in conjunctionwith labeled samples. We employ efficient spectral decomposition algorithms (onmatrices and tensors) for extracting discriminative components. The advantageof employing tensor-valued features is that we can extract richerdiscriminative information in the form of an overcomplete representations.Thus, we present a novel framework for employing generative models of the inputfor discriminative learning.

Score Function Features for Discriminative Learning