NDLI: Hessian regularization based non-negative matrix factorization for gene expression data clustering

Content Provider	IEEE Xplore Digital Library
Author	Xiao Liu Jun Shi Congzhi Wang
Copyright Year	2015
Description	Author affiliation: Shenzhen Inst. of Adv. Technol., Shenzhen, China (Congzhi Wang) \|\| Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China (Xiao Liu; Jun Shi)
Abstract	Since a key step in the analysis of gene expression data is to detect groups of genes that have similar expression patterns, clustering technique is then commonly used to analyze gene expression data. Data representation plays an important role in clustering analysis. The non-negative matrix factorization (NMF) is a widely used data representation method with great success in machine learning. Although the traditional manifold regularization method, Laplacian regularization (LR), can improve the performance of NMF, LR still suffers from the problem of its weak extrapolating power. Hessian regularization (HR) is a newly developed manifold regularization method, whose natural properties make it more extrapolating, especially for small sample data. In this work, we propose the HR-based NMF (HR-NMF) algorithm, and then apply it to represent gene expression data for further clustering task. The clustering experiments are conducted on five commonly used gene datasets, and the results indicate that the proposed HR-NMF outperforms LR-based NMM and original NMF, which suggests the potential application of HR-NMF for gene expression data.
Starting Page	4130
Ending Page	4133
File Size	755617
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319303
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Clustering algorithms Manifolds Laplace equations Linear programming Encoding Accuracy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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