NDLI: Learning the Information Divergence

Content Provider	IEEE Xplore Digital Library
Author	Dikmen, O. Zhirong Yang Oja, E.
Copyright Year	1979
Abstract	Information divergence that measures the difference between two nonnegative matrices or tensors has found its use in a variety of machine learning problems. Examples are Nonnegative Matrix/Tensor Factorization, Stochastic Neighbor Embedding, topic models, and Bayesian network optimization. The success of such a learning task depends heavily on a suitable divergence. A large variety of divergences have been suggested and analyzed, but very few results are available for an objective choice of the optimal divergence for a given task. Here we present a framework that facilitates automatic selection of the best divergence among a given family, based on standard maximum likelihood estimation. We first propose an approximated Tweedie distribution for the β-divergence family. Selecting the best β then becomes a machine learning problem solved by maximum likelihood. Next, we reformulate α-divergence in terms of β-divergence, which enables automatic selection of α by maximum likelihood with reuse of the learning principle for β-divergence. Furthermore, we show the connections between γ- and β-divergences as well as Renyi- and α-divergences, such that our automatic selection framework is extended to non-separable divergences. Experiments on both synthetic and real-world data demonstrate that our method can quite accurately select information divergence across different learning problems and various divergence families.
Sponsorship	IEEE Computer Society
Starting Page	1442
Ending Page	1454
Page Count	13
File Size	1357137
File Format	PDF
ISSN	01628828
Volume Number	37
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Medals Approximation methods Tensile stress Maximum likelihood estimation Stochastic processes Standards Brain modeling stochastic neighbor embedding information divergence Tweedie distribution maximum likelihood nonnegative matrix factorization Information divergence tweedie distribution
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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