Classification methods for high-dimensional genetic data

• Autorzy: Kalina J.

Identyfikatory

DOI

10.1016/j.bbe.2013.09.007

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

multivariate statistics; classification analysis; shrinkage estimation; dimension reduction; data mining

PL

statystyka wielowariancyjna; analiza klasyfikacji; redukcja wymiaru; eksploracja danych

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2014
• Tom: Vol. 34, no. 1
• Strony: 10--18
• Opis fizyczny: Bibliogr. 48 poz.

Twórcy

autor

Kalina J.

• Institute of Computer Science of the Academy of Sciences of the Czech Republic, Department of Medical Informatics and Biostatistics, Prague, Czech Republic

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