Estimating Local Intrinsic Dimensionality

Laurent Amsaleg 1 Oussama Chelly 2 Teddy Furon 1 Stephane Girard 3 Michael E. Houle 2 Ken-Ichi Kawarabayashi 2 Michael Nett 4
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : This paper is concerned with the estimation of continuous intrinsic dimension (ID), a measure of intrinsic dimensionality recently proposed by Houle. Continuous ID can be regarded as an extension of Karger and Ruhl’s expansion dimension to a statistical setting in which the distribution of distances to a query point is modeled in terms of a continuous random variable. This form of intrinsic dimensionality can be particularly useful in search, classification, outlier detection, and other contexts in machine learning, databases, and data mining, as it has been shown to be equivalent to a measure of the discriminative power of similarity functions. Several es- timators of continuous ID are proposed and analyzed based on extreme value theory, using maximum likelihood estimation (MLE), the method of moments (MoM), probability weighted moments (PWM), and regularly varying functions (RV). An experimental evaluation is also provided, using both real and artificial data.
Type de document :
Communication dans un congrès
21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'15, Aug 2015, Sidney, Australia. ACM, pp.29-38 2015, <http://www.kdd.org/kdd2015/>. <10.1145/2783258.2783405>
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https://hal.inria.fr/hal-01159217
Contributeur : Teddy Furon <>
Soumis le : mardi 2 juin 2015 - 18:31:23
Dernière modification le : mercredi 2 août 2017 - 10:09:07

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Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stephane Girard, Michael E. Houle, et al.. Estimating Local Intrinsic Dimensionality. 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'15, Aug 2015, Sidney, Australia. ACM, pp.29-38 2015, <http://www.kdd.org/kdd2015/>. <10.1145/2783258.2783405>. <hal-01159217>

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