A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence

Karthik Devarajan; Nader Ebrahimi; Ehsan Soofi

doi:10.1109/BIBM.2015.7359924

A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence

Karthik Devarajan, Nader Ebrahimi, Ehsan Soofi

Nuclear Dynamics and Cancer (NDC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editors	lng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1658-1664
Number of pages	7
Volume	2015
ISBN (Electronic)	9781467367981
DOIs	https://doi.org/10.1109/BIBM.2015.7359924
State	Published - Dec 16 2015
Event	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States Duration: Nov 9 2015 → Nov 12 2015

Publication series

Name	Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Country/Territory	United States
City	Washington
Period	11/9/15 → 11/12/15

Keywords

Kullback-Leibler divergence
dual
exponential family
intrinsic information
non-negative matrix factorization

Access to Document

10.1109/BIBM.2015.7359924

Cite this

Devarajan, K., Ebrahimi, N., & Soofi, E. (2015). A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence. In L. M. Schapranow, J. Zhou, X. T. Hu, B. Ma, S. Rajasekaran, S. Miyano, I. Yoo, B. Pierce, A. Shehu, V. K. Gombar, B. Chen, V. Pai, & J. Huan (Eds.), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (Vol. 2015, pp. 1658-1664). Article 7359924 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359924

Devarajan, Karthik ; Ebrahimi, Nader ; Soofi, Ehsan. / A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence. Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. editor / lng. Matthieu Schapranow ; Jiayu Zhou ; Xiaohua Tony Hu ; Bin Ma ; Sanguthevar Rajasekaran ; Satoru Miyano ; Illhoi Yoo ; Brian Pierce ; Amarda Shehu ; Vijay K. Gombar ; Brian Chen ; Vinay Pai ; Jun Huan. Vol. 2015 Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1658-1664 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).

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title = "A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence",

abstract = "The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.",

keywords = "Kullback-Leibler divergence, dual, exponential family, intrinsic information, non-negative matrix factorization",

author = "Karthik Devarajan and Nader Ebrahimi and Ehsan Soofi",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

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Devarajan, K, Ebrahimi, N & Soofi, E 2015, A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence. in LM Schapranow, J Zhou, XT Hu, B Ma, S Rajasekaran, S Miyano, I Yoo, B Pierce, A Shehu, VK Gombar, B Chen, V Pai & J Huan (eds), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. vol. 2015, 7359924, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1658-1664, IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Washington, United States, 11/9/15. https://doi.org/10.1109/BIBM.2015.7359924

A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence. / Devarajan, Karthik; Ebrahimi, Nader; Soofi, Ehsan.
Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. ed. / lng. Matthieu Schapranow; Jiayu Zhou; Xiaohua Tony Hu; Bin Ma; Sanguthevar Rajasekaran; Satoru Miyano; Illhoi Yoo; Brian Pierce; Amarda Shehu; Vijay K. Gombar; Brian Chen; Vinay Pai; Jun Huan. Vol. 2015 Institute of Electrical and Electronics Engineers Inc., 2015. p. 1658-1664 7359924 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ebrahimi, Nader

AU - Soofi, Ehsan

PY - 2015/12/16

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N2 - The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.

AB - The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.

KW - Kullback-Leibler divergence

KW - dual

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SP - 1658

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BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

A2 - Schapranow, lng. Matthieu

A2 - Zhou, Jiayu

A2 - Hu, Xiaohua Tony

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A2 - Rajasekaran, Sanguthevar

A2 - Miyano, Satoru

A2 - Yoo, Illhoi

A2 - Pierce, Brian

A2 - Shehu, Amarda

A2 - Gombar, Vijay K.

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A2 - Huan, Jun

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Devarajan K, Ebrahimi N, Soofi E. A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence. In Schapranow LM, Zhou J, Hu XT, Ma B, Rajasekaran S, Miyano S, Yoo I, Pierce B, Shehu A, Gombar VK, Chen B, Pai V, Huan J, editors, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Vol. 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1658-1664. 7359924. (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). doi: 10.1109/BIBM.2015.7359924

A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence

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