NDLI: Kernel PCA regression for missing data estimation in DNA microarray analysis

Content Provider	IEEE Xplore Digital Library
Author	Ying Shan Guang Deng
Copyright Year	2009
Description	Author affiliation: Department of Electronic Engineering, La Trobe University, Bundoora, Victoria, Australia 3086 (Ying Shan; Guang Deng)
Abstract	The DNA microarray may contain missing expression data. Estimation of missing values is a necessary step in microarray analysis, because data mining procedures require a complete expression as their input. In this paper, we propose a missing data estimation algorithm, named KPCAimpute, based on kernel principal component analysis. We consider a family of heavy-tailed kernel functions, which is a generalization of the famous Gaussian kernel. The performance of the proposed KPCAimpute algorithm is compared with two state-of-the-art linear regression methods, i.e., Bayesian principal component analysis imputation (BPCA) and local least squares imputation (LLSimpute). The KPCAimpute outperforms the LL-Simpute when the missing percentage increases. The performance of the KPCAimpute is similar to that of the BPCA imputation. Therefore, it is an effective and promising algorithm in estimating missing values for DNA microarray profiles.
Starting Page	1477
Ending Page	1480
File Size	618493
Page Count	4
File Format	PDF
ISBN	9781424438273
DOI	10.1109/ISCAS.2009.5118046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Principal component analysis DNA Linear regression Bayesian methods Least squares methods Clustering algorithms Gene expression Vectors Data analysis
Content Type	Text
Resource Type	Article

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