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. https://resolver.caltech.edu/CaltechAUTHORS:20190401-162925219
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Abstract
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. | ||||||||||||||
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Subject Keywords: | Feature learning, semi-supervised learning, self-taught learning, pre-training, score function, spectral decomposition methods, tensor methods | ||||||||||||||
Record Number: | CaltechAUTHORS:20190401-162925219 | ||||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190401-162925219 | ||||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||
ID Code: | 94346 | ||||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||||
Deposited By: | George Porter | ||||||||||||||
Deposited On: | 03 Apr 2019 14:37 | ||||||||||||||
Last Modified: | 03 Oct 2019 21:03 |
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