Using topology preservation measures for high-dimensional data analysis in a reduced feature space

Szymon Łukasik,

Piotr Kulczycki

Abstrakt

This paper deals with high-dimensional data analysis accomplished through supplementing standard feature extraction procedures with topology preservation measures. This approach is based on an observation that not all elements of an initial dataset are equally preserved in its low-dimensional embedding space representation. The contribution first overviews existing topology preservation measures, then their inclusion in the classical methods of exploratory data analysis is discussed. Finally, some illustrative examples of presented approach in the tasks of cluster analysis and classification are given.

Słowa kluczowe: multidimensional datasets, dimensionality reduction, topology preservation, cluster analysis, classification