Intrinsic dimension estimation of manifolds by incising balls
Pattern Recognition
Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA
Pattern Recognition Letters
Face detection based on the manifold
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Intrinsic dimension induced similarity measure for clustering
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Intrinsic dimension identification via graph-theoretic methods
Journal of Multivariate Analysis
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The intrinsic dimensionality of a set of patterns is important in determining an appropriate number of features for representing the data and whether a reasonable two- or three-dimensional representation of the data exists. We propose an intuitively appealing, noniterative estimator for intrinsic dimensionality which is based on nearneighbor information. We give plausible arguments supporting the consistency of this estimator. The method works well in identifying the true dimensionality for a variety of artificial data sets and is fairly insensitive to the number of samples and to the algorithmic parameters. Comparisons between this new method and the global eigenvalue approach demonstrate the utility of our estimator.