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Erschienen in:
Efficiency and Effectiveness Metrics in Evolutionary Algorithms and Their Application Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Application of Graph Regularized Non-negative Matrix Factorization in Characteristic Gene Selection

verfasst von : Dong Wang, Ying-Lian Gao, Jin-Xing Liu, Ji-Guo Yu, Chang-Gang Wen

Erschienen in: Intelligent Computing Theories and Methodologies

Verlag: Springer International Publishing

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Abstract

Nonnegative matrix factorization (NMF) has become a popular method and widely used in many fields, for the reason that NMF algorithm can deal with many high dimension, non-negative problems. However, in real gene expression data applications, we often have to deal with the geometric structure problems. Thus a Graph Regularized version of NMF is needed. In this paper, we propose a Graph Regularized Non-negative Matrix Factorization (GRNMF) with emphasizing graph regularized on error function to extract characteristic gene set. This method considers the samples in low-dimensional manifold which embedded in a high-dimensional ambient space, and reveals the data geometric structure embedded in the original data. Experiment results on tumor datasets and plants gene expression data demonstrate that our GRNMF model can extract more differential genes than other existing state-of-the-art methods.

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Vorheriges Kapitel Identification of Mild Cognitive Impairment Using Extreme Learning Machines Model
Nächstes Kapitel Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes
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Metadaten
Titel
Application of Graph Regularized Non-negative Matrix Factorization in Characteristic Gene Selection
verfasst von
Dong Wang
Ying-Lian Gao
Jin-Xing Liu
Ji-Guo Yu
Chang-Gang Wen
Copyright-Jahr
2015
Buch
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22185-4

Electronic ISBN: 978-3-319-22186-1

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-22186-1_60