Sparse principal balances

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F15%3A33155230" target="_blank" >RIV/61989592:15310/15:33155230 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1177/1471082X14535525" target="_blank" >http://dx.doi.org/10.1177/1471082X14535525</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/1471082X14535525" target="_blank" >10.1177/1471082X14535525</a>

Result language
angličtina
Original language name
Sparse principal balances
Original language description
Compositional data analysis deals with situations where the relevant information is contained only in the ratios between the measured variables, and not in the reported values. This article focuses on high-dimensional compositional data (in the sense ofhundreds or even thousands of variables), as they appear in chemometrics (e.g., mass spectral data), proteomics or genomics. The goal of this contribution is to perform a dimension reduction of such data, where the new directions should allow for interpretability. An approach named principal balances turned out to be successful for low dimensions. Here, the concept of sparse principal component analysis is proposed for constructing principal directions, the so-called sparse principal balances. They aresparse (contain many zeros), build an orthonormal basis in the sample space of the compositional data, are efficient for dimension reduction and are applicable to high-dimensional data.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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