Comparison of non-negative matrix factorization methods for clustering genomic data

Mi Xiao Hou; Ying Lian Gao; Jin Xing Liu; Jun Liang Shang; Chun Hou Zheng

doi:10.1007/978-3-319-42294-7_25

Comparison of non-negative matrix factorization methods for clustering genomic data

Mi Xiao Hou
, Ying Lian Gao
, Jin Xing Liu
, Jun Liang Shang
, Chun Hou Zheng

Qufu Normal University
Harbin Institute of Technology

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

3 Scopus citations

Abstract

Non-negative matrix factorization (NMF) is a useful method of data dimensionality reduction and has been widely used in many fields, such as pattern recognition and data mining. Compared with other traditional methods, it has unique advantages. And more and more improved NMF methods have been provided in recent years and all of these methods have merits and demerits when used in different applications. Clustering based on NMF methods is a common way to reflect the properties of methods. While there are no special comparisons of clustering experiments based on NMF methods on genomic data. In this paper, we analyze the characteristics of basic NMF and its classical variant methods. Moreover, we show the clustering results based on the coefficient matrix decomposed by NMF methods on the genomic datasets. We also compare the clustering accuracies and the cost of time of these methods.

Original language	English
Title of host publication	Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings
Editors	De-Shuang Huang, Kang-Hyun Jo
Publisher	Springer Verlag
Pages	290-299
Number of pages	10
ISBN (Print)	9783319422930
DOIs	https://doi.org/10.1007/978-3-319-42294-7_25
State	Published - 2016
Externally published	Yes
Event	12th International Conference on Intelligent Computing Theories and Application, ICIC 2016 - Lanzhou, China Duration: 2 Aug 2016 → 5 Aug 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9772
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Intelligent Computing Theories and Application, ICIC 2016
Country/Territory	China
City	Lanzhou
Period	2/08/16 → 5/08/16

Keywords

Clustering
Dimensionality reduction
Genomic data
Non-negative matrix factorization

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10.1007/978-3-319-42294-7_25

Cite this

Hou, M. X., Gao, Y. L., Liu, J. X., Shang, J. L., & Zheng, C. H. (2016). Comparison of non-negative matrix factorization methods for clustering genomic data. In D.-S. Huang, & K.-H. Jo (Eds.), Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings (pp. 290-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9772). Springer Verlag. https://doi.org/10.1007/978-3-319-42294-7_25

Hou, Mi Xiao ; Gao, Ying Lian ; Liu, Jin Xing et al. / Comparison of non-negative matrix factorization methods for clustering genomic data. Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings. editor / De-Shuang Huang ; Kang-Hyun Jo. Springer Verlag, 2016. pp. 290-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Comparison of non-negative matrix factorization methods for clustering genomic data",

abstract = "Non-negative matrix factorization (NMF) is a useful method of data dimensionality reduction and has been widely used in many fields, such as pattern recognition and data mining. Compared with other traditional methods, it has unique advantages. And more and more improved NMF methods have been provided in recent years and all of these methods have merits and demerits when used in different applications. Clustering based on NMF methods is a common way to reflect the properties of methods. While there are no special comparisons of clustering experiments based on NMF methods on genomic data. In this paper, we analyze the characteristics of basic NMF and its classical variant methods. Moreover, we show the clustering results based on the coefficient matrix decomposed by NMF methods on the genomic datasets. We also compare the clustering accuracies and the cost of time of these methods.",

keywords = "Clustering, Dimensionality reduction, Genomic data, Non-negative matrix factorization",

author = "Hou, \{Mi Xiao\} and Gao, \{Ying Lian\} and Liu, \{Jin Xing\} and Shang, \{Jun Liang\} and Zheng, \{Chun Hou\}",

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doi = "10.1007/978-3-319-42294-7\_25",

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Hou, MX, Gao, YL, Liu, JX, Shang, JL & Zheng, CH 2016, Comparison of non-negative matrix factorization methods for clustering genomic data. in D-S Huang & K-H Jo (eds), Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9772, Springer Verlag, pp. 290-299, 12th International Conference on Intelligent Computing Theories and Application, ICIC 2016, Lanzhou, China, 2/08/16. https://doi.org/10.1007/978-3-319-42294-7_25

Comparison of non-negative matrix factorization methods for clustering genomic data. / Hou, Mi Xiao; Gao, Ying Lian; Liu, Jin Xing et al.
Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings. ed. / De-Shuang Huang; Kang-Hyun Jo. Springer Verlag, 2016. p. 290-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9772).

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

TY - GEN

T1 - Comparison of non-negative matrix factorization methods for clustering genomic data

AU - Hou, Mi Xiao

AU - Gao, Ying Lian

AU - Liu, Jin Xing

AU - Shang, Jun Liang

AU - Zheng, Chun Hou

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - Non-negative matrix factorization (NMF) is a useful method of data dimensionality reduction and has been widely used in many fields, such as pattern recognition and data mining. Compared with other traditional methods, it has unique advantages. And more and more improved NMF methods have been provided in recent years and all of these methods have merits and demerits when used in different applications. Clustering based on NMF methods is a common way to reflect the properties of methods. While there are no special comparisons of clustering experiments based on NMF methods on genomic data. In this paper, we analyze the characteristics of basic NMF and its classical variant methods. Moreover, we show the clustering results based on the coefficient matrix decomposed by NMF methods on the genomic datasets. We also compare the clustering accuracies and the cost of time of these methods.

AB - Non-negative matrix factorization (NMF) is a useful method of data dimensionality reduction and has been widely used in many fields, such as pattern recognition and data mining. Compared with other traditional methods, it has unique advantages. And more and more improved NMF methods have been provided in recent years and all of these methods have merits and demerits when used in different applications. Clustering based on NMF methods is a common way to reflect the properties of methods. While there are no special comparisons of clustering experiments based on NMF methods on genomic data. In this paper, we analyze the characteristics of basic NMF and its classical variant methods. Moreover, we show the clustering results based on the coefficient matrix decomposed by NMF methods on the genomic datasets. We also compare the clustering accuracies and the cost of time of these methods.

KW - Clustering

KW - Dimensionality reduction

KW - Genomic data

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BT - Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings

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ER -

Hou MX, Gao YL, Liu JX, Shang JL, Zheng CH. Comparison of non-negative matrix factorization methods for clustering genomic data. In Huang DS, Jo KH, editors, Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings. Springer Verlag. 2016. p. 290-299. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42294-7_25

Comparison of non-negative matrix factorization methods for clustering genomic data

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