Basis exchange and learning algorithms for extracting collinear patterns

ABSTRACT 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.