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Conference papers

Estimating Local Intrinsic Dimensionality

Laurent Amsaleg 1 Oussama Chelly 2 Teddy Furon 1 Stéphane 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, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
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.
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Submitted on : Tuesday, June 2, 2015 - 6:31:23 PM
Last modification on : Thursday, January 20, 2022 - 5:28:03 PM



Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle, et al.. Estimating Local Intrinsic Dimensionality. 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'15, ACM, Aug 2015, Sidney, Australia. pp.29-38 ⟨10.1145/2783258.2783405⟩. ⟨hal-01159217⟩



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