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basis exchange and learning algorithms for extracting collinear patterns

JOURNAL OF INFORMATION AND TELECOMMUNICATION 2021年第3期5卷 334-349页

作者： Bobrowski, Leon Zabielski, Pawel Bialystok Tech Univ Fac Comp Sci Bialystok Poland PAS Inst Biocybernet & Biomed Engn Warsaw Poland

Understanding large data sets is one of the most important and challenging problems in the modern days. Exploration of genetic data sets composed of high dimensional feature vectors can be treated as a leading example in this context. A better understanding of large, multivariate data sets can be achieved through exploration and extraction of their structure. Collinear patterns can be an important part of a given data set structure. Collinear (flat) pattern exists in a given set of feature vectors when many of these vectors are located on (or near) some plane in the feature space. Discovered flat patterns can reflect various types of interaction in an explored data set. The presented paper compares basis exchange algorithms with learning algorithms in the task of flat patterns extraction.

关键词： Data exploration flat patterns extraction CPL criterion functions basis exchange algorithms learning algorithms

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basis exchange and learning algorithms for extracting collinear patterns

引用

Journal of Information and Telecommunication 2018年第1期2卷 53-67页

作者： Bobrowski, Leon Zabielski, Paweł Faculty of Computer Science Bialystok University of Technology Bialystok Poland Institute of Biocybernetics and Biomedical Engineering PAS Warsaw Poland

Understanding large data sets is one of the most important and challenging problem in modern days. Exploration of genetic data sets composed of high-dimensional feature vectors can be treated as a leading example in this context. A better understanding of large, multivariate data sets can be achieved through exploration and extraction of their structure. Collinear patterns can be an important part of a given data set structure. Collinear (flat) patterns exist in a given set of feature vectors when many of these vectors are located on (or near) some planes in the feature space. Discovered flat patterns can reflect various types of interaction in an explored data set. The presented paper compares basis exchange algorithms with learning algorithms in the task of flat patterns extraction. © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： basis exchange algorithms CPL criterion functions Data exploration Flat patterns extraction Learning algorithms

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