Independent Component Analysis for Compositional Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73609685" target="_blank" >RIV/61989592:15310/21:73609685 - isvavai.cz</a>
Result on the web
<a href="https://obd.upol.cz/id_publ/333189571" target="_blank" >https://obd.upol.cz/id_publ/333189571</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-73249-3_27" target="_blank" >10.1007/978-3-030-73249-3_27</a>
Alternative languages
Result language
angličtina
Original language name
Independent Component Analysis for Compositional Data
Original language description
Compositional data represent a specific family of multivariate data, where the information of interest is contained in the ratios between parts rather than in absolute values of single parts. The analysis of such specific data is challenging as the application of standard multivariate analysis tools on the raw observations can lead to spurious results. Hence, it is appropriate to apply certain transformations prior to further analysis. One popular multivariate data analysis tool is independent component analysis. Independent component analysis aims to find statistically independent components in the data and as such might be seen as an extension to principal component analysis. In this paper, we examine an approach of how to apply independent component analysis on compositional data by respecting the nature of the latter and demonstrate the usefulness of this procedure on a metabolomics dataset.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Advances in Contemporary Statistics and Econometrics, Festschrift in Honor of Christine Thomas-Agnan
ISBN
978-3-030-73248-6
Number of pages of the result
21
Pages from-to
525-545
Number of pages of the book
710
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
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