Clustering with a semantic criterion based on dimensionality analysis

Authors:
Wenye Li;Kin-Hong Lee;Kwong-Sak Leung
Affiliations:
The Chinese University of Hong Kong, Shatin N.T., Hong Kong, China P.R.;The Chinese University of Hong Kong, Shatin N.T., Hong Kong, China P.R.;The Chinese University of Hong Kong, Shatin N.T., Hong Kong, China P.R.
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

Considering data processing problems from a geometric point of view, previous work has shown that the intrinsic dimension of the data could have some semantics. In this paper, we start from the consideration of this inherent topology property and propose the usage of such a semantic criterion for clustering. The corresponding learning algorithms are provided. Theoretical justification and analysis of the algorithms are shown. Promising results are reported by the experiments that generally fail with conventional clustering algorithms.