2015 EstimatingLocalIntrinsicDimensi

• (Amsaleg et al., 2015) ⇒ Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle, Ken-ichi Kawarabayashi, and Michael Nett. (2015). “Estimating Local Intrinsic Dimensionality.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783405

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Estimating+Local+Intrinsic+Dimensionality
• http://dl.acm.org/citation.cfm?id=2783258.2783405&preflayout=flat#citedby

Quotes

Author Keywords

• Distribution functions; indiscriminability; intrinsic dimension; manifold learning; parameter learning

Abstract

This paper is concerned with the estimation of a local measure of intrinsic dimensionality (ID) recently proposed by Houle. The local model 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 estimators of local 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 EstimatingLocalIntrinsicDimensi	Laurent Amsaleg Oussama Chelly Teddy Furon Stéphane Girard Michael E. Houle Ken-ichi Kawarabayashi Michael Nett			Estimating Local Intrinsic Dimensionality				10.1145/2783258.2783405		2015