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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytické základy neurovýpočtů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Biocybernetics and Biomedical Engineering

ISSN

0208-5216

e-ISSN

—

Svazek periodika

40

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

13

Strana od-do

774-786

Kód UT WoS článku

000547542400014

EID výsledku v databázi Scopus

2-s2.0-85084491501