A Sparse Pair-preserving Centroid-based Supervised Learning Method for High-dimensional Biomedical Data or Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00524330" target="_blank" >RIV/67985807:_____/20:00524330 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.bbe.2020.03.008" target="_blank" >http://dx.doi.org/10.1016/j.bbe.2020.03.008</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bbe.2020.03.008" target="_blank" >10.1016/j.bbe.2020.03.008</a>

Result language

angličtina

Original language name

A Sparse Pair-preserving Centroid-based Supervised Learning Method for High-dimensional Biomedical Data or Images

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytical Foundations of Neurocomputing</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Biocybernetics and Biomedical Engineering

ISSN

0208-5216

e-ISSN

—

Volume of the periodical

40

Issue of the periodical within the volume

2

Country of publishing house

PL - POLAND

Number of pages

13

Pages from-to

774-786

UT code for WoS article

000547542400014

EID of the result in the Scopus database

2-s2.0-85084491501