Title :
Intrinsic dimensionality estimation with optimally topology preserving maps
Author :
Bruske, J. ; Sommer, G.
Author_Institution :
Comput. Sci. Inst., Kiel Univ., Germany
fDate :
5/1/1998 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
