Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection

Ling Yun Dai; Chun Mei Feng; Jin Xing Liu; Chun Hou Zheng; Mi Xiao Hou; Jiguo Yu

doi:10.1109/BIBM.2016.7822698

Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection

Ling Yun Dai
, Chun Mei Feng
, Jin Xing Liu
, Chun Hou Zheng
, Mi Xiao Hou
, Jiguo Yu

Qufu Normal University

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

1 Scopus citations

Abstract

Recent research shows that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because vast amount of gene expression data have been generated with the development of gene detection technology. Nonetheless, the recognition rate and reliability of gene selection still need to be improved. In this paper, we propose a novel constrained method: robust graph regularized discriminative nonnegative matrix factorization (RGDNMF) for characteristic gene selection. The method mainly includes two aspects: firstly, we incorporate both intrinsic geometrical structure and discriminative label information into the NMF model. Secondly, we adopt L2,1 -norm minimization to both the error function and the regularization term which is robust to noises and outliers in gene data. Furthermore we present the multiplicative update rules and the convergence proof. Our experiments demonstrate that RGDNMF is far more effective than other existing methods.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1253-1258
Number of pages	6
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822698
State	Published - 17 Jan 2017
Externally published	Yes
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Keywords

Discriminative information
Gene selection
Manifold embed
Nonnegative matrix factorization

Access to Document

10.1109/BIBM.2016.7822698

Cite this

Dai, L. Y., Feng, C. M., Liu, J. X., Zheng, C. H., Hou, M. X., & Yu, J. (2017). Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1253-1258). Article 7822698 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822698

Dai, Ling Yun ; Feng, Chun Mei ; Liu, Jin Xing et al. / Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1253-1258 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{1e9e0c7da31f4b559d519daaa01acec8,

title = "Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection",

abstract = "Recent research shows that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because vast amount of gene expression data have been generated with the development of gene detection technology. Nonetheless, the recognition rate and reliability of gene selection still need to be improved. In this paper, we propose a novel constrained method: robust graph regularized discriminative nonnegative matrix factorization (RGDNMF) for characteristic gene selection. The method mainly includes two aspects: firstly, we incorporate both intrinsic geometrical structure and discriminative label information into the NMF model. Secondly, we adopt L2,1 -norm minimization to both the error function and the regularization term which is robust to noises and outliers in gene data. Furthermore we present the multiplicative update rules and the convergence proof. Our experiments demonstrate that RGDNMF is far more effective than other existing methods.",

keywords = "Discriminative information, Gene selection, Manifold embed, Nonnegative matrix factorization",

author = "Dai, \{Ling Yun\} and Feng, \{Chun Mei\} and Liu, \{Jin Xing\} and Zheng, \{Chun Hou\} and Hou, \{Mi Xiao\} and Jiguo Yu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822698",

language = "英语",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1253--1258",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, \{Xiaohua Tony\} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

}

Dai, LY, Feng, CM, Liu, JX, Zheng, CH, Hou, MX & Yu, J 2017, Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822698, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1253-1258, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822698

Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection. / Dai, Ling Yun; Feng, Chun Mei; Liu, Jin Xing et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1253-1258 7822698 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

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

TY - GEN

T1 - Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection

AU - Dai, Ling Yun

AU - Feng, Chun Mei

AU - Liu, Jin Xing

AU - Zheng, Chun Hou

AU - Hou, Mi Xiao

AU - Yu, Jiguo

PY - 2017/1/17

Y1 - 2017/1/17

N2 - Recent research shows that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because vast amount of gene expression data have been generated with the development of gene detection technology. Nonetheless, the recognition rate and reliability of gene selection still need to be improved. In this paper, we propose a novel constrained method: robust graph regularized discriminative nonnegative matrix factorization (RGDNMF) for characteristic gene selection. The method mainly includes two aspects: firstly, we incorporate both intrinsic geometrical structure and discriminative label information into the NMF model. Secondly, we adopt L2,1 -norm minimization to both the error function and the regularization term which is robust to noises and outliers in gene data. Furthermore we present the multiplicative update rules and the convergence proof. Our experiments demonstrate that RGDNMF is far more effective than other existing methods.

AB - Recent research shows that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because vast amount of gene expression data have been generated with the development of gene detection technology. Nonetheless, the recognition rate and reliability of gene selection still need to be improved. In this paper, we propose a novel constrained method: robust graph regularized discriminative nonnegative matrix factorization (RGDNMF) for characteristic gene selection. The method mainly includes two aspects: firstly, we incorporate both intrinsic geometrical structure and discriminative label information into the NMF model. Secondly, we adopt L2,1 -norm minimization to both the error function and the regularization term which is robust to noises and outliers in gene data. Furthermore we present the multiplicative update rules and the convergence proof. Our experiments demonstrate that RGDNMF is far more effective than other existing methods.

KW - Discriminative information

KW - Gene selection

KW - Manifold embed

KW - Nonnegative matrix factorization

UR - https://www.scopus.com/pages/publications/85013249570

U2 - 10.1109/BIBM.2016.7822698

DO - 10.1109/BIBM.2016.7822698

M3 - 会议稿件

AN - SCOPUS:85013249570

T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 1253

EP - 1258

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

A2 - Burrage, Kevin

A2 - Zhu, Qian

A2 - Liu, Yunlong

A2 - Tian, Tianhai

A2 - Wang, Yadong

A2 - Hu, Xiaohua Tony

A2 - Jiang, Qinghua

A2 - Song, Jiangning

A2 - Morishita, Shinichi

A2 - Burrage, Kevin

A2 - Wang, Guohua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Y2 - 15 December 2016 through 18 December 2016

ER -

Dai LY, Feng CM, Liu JX, Zheng CH, Hou MX, Yu J. Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1253-1258. 7822698. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822698

Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection

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