DocumentCode
1389473
Title
Intrinsic dimensionality estimation with optimally topology preserving maps
Author
Bruske, J. ; Sommer, G.
Author_Institution
Comput. Sci. Inst., Kiel Univ., Germany
Volume
20
Issue
5
fYear
1998
fDate
5/1/1998 12:00:00 AM
Firstpage
572
Lastpage
575
Abstract
A new method for analyzing the intrinsic dimensionality (ID) of low-dimensional manifolds in high-dimensional feature spaces is presented. Compared to a previous approach by Fukunaga and Olsen (1971), the method has only linear instead of cubic time complexity with respect to the dimensionality of the input space. Moreover, it is less sensitive to noise than the former approach. Experiments include ID estimation of synthetic data for comparison and illustration as well as ID estimation of an image sequence
Keywords
computational complexity; eigenvalues and eigenfunctions; estimation theory; image sequences; pattern classification; topology; vector quantisation; eigenvalues; image sequence; intrinsic dimensionality; pattern classification; principal component analysis; time complexity; topology preservation; vector quantization; Data visualization; Fractals; Image sequences; Monitoring; Neural networks; Nonlinear distortion; Principal component analysis; System identification; Topology; Vector quantization;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.682189
Filename
682189
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