Geodesic distances in the maximum likelihood estimator of intrinsic dimensionality
Rasa Karbauskaitė
Vilnius University, Lithuania
Gintautas Dzemyda
Vilnius University, Lithuania
Edmundas Mazėtis
Lithuanian University of Educational Sciences, Lithuania
Published 2011-12-07


multidimensional data
intrinsic dimensionality
maximum likelihood estimator

How to Cite

Karbauskaitė R., Dzemyda G. and Mazėtis E. (2011) “Geodesic distances in the maximum likelihood estimator of intrinsic dimensionality”, Nonlinear Analysis: Modelling and Control, 16(4), pp. 387-402. doi: 10.15388/NA.16.4.14084.


While analyzing multidimensional data, we often have to reduce their dimensionality so that to preserve as much information on the analyzed data set as possible. To this end, it is reasonable to find out the intrinsic dimensionality of the data. In this paper, two techniques for the intrinsic dimensionality are analyzed and compared, i.e., the maximum likelihood estimator (MLE) and ISOMAP method. We also propose the way how to get good estimates of the intrinsic dimensionality by the MLE method.

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