Geodesic distances in the intrinsic dimensionality estimation using packing numbers
Articles
Rasa Karbauskaitė
Vilnius University
Gintautas Dzemyda
Vilnius University
Published 2014-07-07
https://doi.org/10.15388/NA.2014.4.4
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Keywords

multidimensional data
intrinsic dimensionality
packing numbers estimator
manifold
degrees of freedom
image understanding
motion

How to Cite

Karbauskaitė R. and Dzemyda G. (2014) “Geodesic distances in the intrinsic dimensionality estimation using packing numbers”, Nonlinear Analysis: Modelling and Control, 19(4), pp. 578-591. doi: 10.15388/NA.2014.4.4.

Abstract

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.

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