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Score Function Features for Discriminative Learning

Janzamin, Majid and Sedghi, Hanie and Anandkumar, Anima (2015) Score Function Features for Discriminative Learning. In: 3rd International Conference on Learning Representations (ICLR 2015), 7-9 May 2015, San Diego, CA.

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Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms for extracting discriminative information, given these pre-trained features and labeled samples for any related task. Our class of features are based on higher-order score functions, which capture local variations in the probability density function of the input. We establish a theoretical framework to characterize the nature of discriminative information that can be extracted from score-function features, when used in conjunction with labeled samples. We employ efficient spectral decomposition algorithms (on matrices and tensors) for extracting discriminative components. The advantage of employing tensor-valued features is that we can extract richer discriminative information in the form of an overcomplete representations. Thus, we present a novel framework for employing generative models of the input for discriminative learning.

Item Type:Conference or Workshop Item (Paper)
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Additional Information:M. Janzamin thanks Rina Panigrahy for useful discussions. M. Janzamin is supported by NSF Award CCF-1219234. H. Sedghi is supported by ONR Award N00014-14-1-0665. A. Anandkumar is supported in part by Microsoft Faculty Fellowship, NSF Career award CCF-1254106, NSF Award CCF-1219234, ARO YIP Award W911NF-13-1-0084 and ONR Award N00014-14-1-0665.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-14-1-0665
Microsoft Faculty FellowshipUNSPECIFIED
Army Research Office (ARO)W911NF-13-1-0084
Subject Keywords:Feature learning, semi-supervised learning, self-taught learning, pre-training, score function, spectral decomposition methods, tensor methods
Record Number:CaltechAUTHORS:20190401-162925219
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94346
Deposited By: George Porter
Deposited On:03 Apr 2019 14:37
Last Modified:03 Oct 2019 21:03

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