OREGON STATE UNIVERSITY

You are here

On Local Intrinsic Dimension Estimation and Its Applications

TitleOn Local Intrinsic Dimension Estimation and Its Applications
Publication TypeJournal Article
Year of Publication2010
AuthorsCarter, K. M., R. Raich, and A. O. Hero, III
JournalIEEE Transactions on Signal Processing
Volume58
Issue2
Pagination650 - 663
Date Published02/2010
ISSN1941-0476
Keywordsgeodesics, image segmentation, intrinsic dimension, manifold learning, nearest neighbor graph
Abstract

In this paper, we present multiple novel applications for local intrinsic dimension estimation. There has been much work done on estimating the global dimension of a data set, typically for the purposes of dimensionality reduction. We show that by estimating dimension locally, we are able to extend the uses of dimension estimation to many applications, which are not possible with global dimension estimation. Additionally, we show that local dimension estimation can be used to obtain a better global dimension estimate, alleviating the negative bias that is common to all known dimension estimation algorithms. We illustrate local dimension estimation's uses towards additional applications, such as learning on statistical manifolds, network anomaly detection, clustering, and image segmentation.

DOI10.1109/TSP.2009.2031722
Short TitleIEEE Trans. Signal Process.