Detecting critical regions in multidimensional data sets

0.00 Avg rating—0 Votes

Article ID:	iaor20112148
Volume:	61
Issue:	2
Start Page Number:	499
End Page Number:	512
Publication Date:	Jan 2011
Journal:	Computers and Mathematics with Applications
Authors:	Allili Madjid, Corriveau David, Derivire Sara, Ethier Marc, Kaczynski Tomasz
Keywords:	statistics: general, statistics: multivariate

Abstract:

We propose a new approach, based on the Conley index theory, for the detection and classification of critical regions in multidimensional data sets. The use of homology groups makes this method consistent and successful in all dimensions and allows us to generalize visual classification techniques based solely on the notion of connectedness which may fail in higher dimensions.

Reviews

Required fields are marked *. Your email address will not be published.