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Dimensionality Reduction Methods for Biomedical Data
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SYSNO ASEP 0491813 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve SCOPUS Title Dimensionality Reduction Methods for Biomedical Data Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Schlenker, A. (CZ)Source Title Lékař a technika. Biomedicinské inženýrství a informatika. - : Česká lékařská společnost J. E. Purkyně - ISSN 0301-5491
Roč. 48, č. 1 (2018), s. 29-35Number of pages 7 s. Language eng - English Country CZ - Czech Republic Keywords biomedical data ; dimensionality ; biostatistics ; multivariate analysis ; sparsity Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects NV15-29835A GA MZd - Ministry of Health (MZ) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 85049794593 Annotation The aim of this paper is to present basic principles of common multivariate statistical approaches to dimensionality reduction and to discuss three particular approaches, namely feature extraction, (prior) variable selection, and sparse variable selection. Their important examples are also presented in the paper, which includes the principal component analysis, minimum redundancy maximum relevance variable selection, and nearest shrunken centroid classifier with an intrinsic variable selection. Each of the three methods is illustrated on a real dataset with a biomedical motivation, including a biometric identification based on keystroke dynamics or a study of metabolomic profiles. Advantages and benefits of performing dimensionality reduction of multivariate data are discussed. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019 Electronic address https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/4425/4722
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