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Classification methods for high-dimensional genetic data

Paper ID Volume ID Publish Year Pages File Format Full-Text
5231 353 2014 9 PDF Available
Title
Classification methods for high-dimensional genetic data
Abstract

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.

Keywords
Multivariate statistics; Classification analysis; Shrinkage estimation; Dimension reduction; Data mining
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Classification methods for high-dimensional genetic data
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 34, Issue 1, 2014, Pages 10–18
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us