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ABSTRACT
<p><b>Abstract</b>—A new method for analyzing the <it>intrinsic dimensionality</it> (ID) of low-dimensional manifolds in high-dimensional feature spaces is presented. Compared to a previous approach by Fukunaga and Olsen, the method has only <it>linear instead of cubic time complexity</it> w.r.t. the dimensionality of the input space. Moreover, it is <it>less sensitive to noise</it> than the former approach. Experiments include ID estimation of synthetic data for comparison and illustration as well as ID estimation of an image sequence.</p>
INDEX TERMS
Intrinsic dimensionality estimation, topology preservation, principal component analysis, vector quantization.
CITATION

G. Sommer and J. Bruske, "Intrinsic Dimensionality Estimation With Optimally Topology Preserving Maps," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 572-575, 1998.
doi:10.1109/34.682189
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