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Classification methods for high-dimensional genetic data
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DOI：10.1016/j.bbe.2013.09.007.png)
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Abstract
En 中文
Standard methods of multivariate statistics fail in the analysis of high-dimensional data. This paper gives an overview of recent classification methods proposed for the analysis of high-dimensional data, especially in the context of molecular genetics. We discuss methods of both biostatistics and data mining based on various background, explain their principles, and compare their advantages and limitations. We also include dimension reduction methods tailor-made for classification analysis and also such classification methods which reduce the dimension of the computation intrinsically. A common feature of numerous classification methods is the shrinkage estimation principle, which has obtained a recent intensive attention in high-dimensional applications. (C) 2013 Nalecz Institute of Biocybernetics and Biomedical Engineering. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.
Keywords:
Multivariate statistics
Classification analysis
Shrinkage estimation
Dimension reduction
Data mining
Journal
IF:
6.6
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937
Citations:
3.3K