Robustness Aspects of Optimized Centroids

0560809 - ÚI 2023 PT eng A - Abstrakt
Kalina, Jan - Janáček, Patrik
Robustness Aspects of Optimized Centroids.
IFCS 2022: Classification and Data Science in the Digital Age. Book of Abstracts. Porto: CLAD - Associação Portuguesa de Classificação e Análise de Dados, 2022. s. 187-187. ISBN 978-989-98955-9-1.
[IFCS 2022: The Conference of the International Federation of Classification Societies /17./. 19.07.2022-23.07.2022, Porto]
Institucionální podpora: RVO:67985807
Klíčová slova: centroids * weighted correlation * robustness * contamination * centroid optimization
https://ifcs2022.fep.up.pt/wp-content/uploads/2022/07/IFCS2022_Book_Abstracts_v1.pdf

Centroids are often used for object localization tasks, supervised segmentation in medical image analysis, or classification in other specific tasks. This paper starts by contributing to the theory of centroids by evaluating the effect of modified illumination on the weighted correlation coefficient. Further, robustness of various centroid-based tools is investigated in experiments related to mouth localization in non-standardized facial images or classification of high-dimensional data in a matched pairs design. The most robust results are obtained if the sparse centroidbased method for supervised learning is accompanied with an intrinsic variable selection. Robustness, sparsity, and energy-efficient computation turn out not to contradict the requirement on the optimal performance of the centroids.
Trvalý link: https://hdl.handle.net/11104/0333589