Intrinsic Dimensionality

This entry for the SIGSPATIAL Special July 2010 issue on Similarity Searching in Metric Spaces discusses the notion of intrinsic dimensionality of data in the context of similarity search.Comment: 4 pages, 4 figures, latex; diagram (c) has been correcte

Geodesic distances in the intrinsic dimensionality estimation using packing numbers

Dimensionality reduction is a very important tool in data mining. An intrinsic dimensionality of a data set is a key parameter in many dimensionality reduction algorithms. When the intrinsic dimensionality of a data set is known, it is possible to reduce the dimensionality of the data without losing much information. To this end, it is reasonable to find out the intrinsic dimensionality of the data. In this paper, one of the global estimators of intrinsic dimensionality, the packing numbers estimator (PNE), is explored experimentally. We propose the modification of the PNE method that uses geodesic distances in order to improve the estimates of the intrinsic dimensionality by the PNE method