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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9772)


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.


Non-negative matrix factorization Clustering Genomic data Dimensionality reduction 



This work was supported in part by the NSFC under grant Nos. 61572284, 61502272, 61572283 and 61272339; the Shandong Provincial Natural Science Foundation, under grant Nos. BS2014DX004 and BS2014DX005; Shenzhen Municipal Science and Technology Innovation Council (No. JCYJ20140417172417174); the Project of Shandong Province Higher Educational Science and Technology Program (No. J13LN31).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mi-Xiao Hou
    • 1
  • Ying-Lian Gao
    • 2
  • Jin-Xing Liu
    • 1
    • 3
  • Jun-Liang Shang
    • 1
  • Chun-Hou Zheng
    • 1
  1. 1.School of Information Science and EngineeringQufu Normal UniversityRizhaoChina
  2. 2.Library of Qufu Normal UniversityQufu Normal UniversityRizhaoChina
  3. 3.Shenzhen Graduate School, Bio-Computing Research CenterHarbin Institute of TechnologyShenzhenChina

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