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A Sparse Pair-preserving Centroid-based Supervised Learning Method for High-dimensional Biomedical Data or Images
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SYSNO ASEP 0524330 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title A Sparse Pair-preserving Centroid-based Supervised Learning Method for High-dimensional Biomedical Data or Images Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Matonoha, Ctirad (UIVT-O) RID, SAINumber of authors 2 Source Title Biocybernetics and Biomedical Engineering. - : Elsevier - ISSN 0208-5216
Roč. 40, č. 2 (2020), s. 774-786Number of pages 13 s. Publication form Print - P Language eng - English Country PL - Poland Keywords supervised learning ; high-dimensional data ; robustness ; sparsity ; nonlinear optimization 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 GA19-05704S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UIVT-O - RVO:67985807 UT WOS 000547542400014 EID SCOPUS 85084491501 DOI 10.1016/j.bbe.2020.03.008 Annotation In various biomedical applications designed to compare two groups (e.g. patients and controls in matched case-control studies), it is often desirable to perform a dimensionality reduction in order to learn a classification rule over high-dimensional data. This paper considers a centroid-based classification method for paired data, which at the same time performs a supervised variable selection respecting the matched pairs design. We propose an algorithm for optimizing the centroid (prototype, template). A subsequent optimization of weights for the centroid ensures sparsity, robustness to outliers, and clear interpretation of the contribution of individual variables to the classification task. We apply the method to a simulated matched case-control study dataset, to a gene expression study of acute myocardial infarction, and to mouth localization in 2D facial images. The novel approach yields a comparable performance with standard classifiers and outperforms them if the data are contaminated by outliers. This robustness makes the method relevant for genomic, metabolomic or proteomic high-dimensional data (in matched case-control studies) or medical diagnostics based on images, as (excessive) noise and contamination are ubiquitous in biomedical measurements. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021 Electronic address http://dx.doi.org/10.1016/j.bbe.2020.03.008
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