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

0524330 - ÚI 2021 RIV PL eng J - Journal Article
Kalina, Jan - Matonoha, Ctirad
A Sparse Pair-preserving Centroid-based Supervised Learning Method for High-dimensional Biomedical Data or Images.
Biocybernetics and Biomedical Engineering. Roč. 40, č. 2 (2020), s. 774-786. ISSN 0208-5216. E-ISSN 0208-5216
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional support: RVO:67985807
Keywords : supervised learning * high-dimensional data * robustness * sparsity * nonlinear optimization
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 4.314, year: 2020
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.bbe.2020.03.008

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.
Permanent Link: http://hdl.handle.net/11104/0308691