Estimation of intrinsic dimensionality using high-rate vector quantization


December 12, 2005


Maxim Raginsky


Beckman Institute, University of Illinois


Joint work with Svetlana Lazebnik at UIUC.

In this talk, I will describe a technique for dimensionality estimation based on the notion of quantization dimension, which connects the asymptotic optimal quantization error for a probability distribution on a manifold to its intrinsic dimension. The definition of quantization dimension yields a family of estimation algorithms, whose limiting case is equivalent to a recent method based on packing numbers. Using the formalism of high-rate vector quantization, I will discuss issues of statistical consistency and sensitivity to noise, and present results on real and simulated data.


Maxim Raginsky

Maxim Raginsky received the BS and the MS degrees in 2000 and the PhD degree in 2002 from Northwestern University, all in electrical engineering. He is currently a Beckman Institute Postdoctoral Fellow at the University of Illinois, Urbana-Champaign. His research interests include information theory, statistical learning and pattern recognition (with applications to information geometry of multidimensional signal processing, artificial intelligence and computational neuroscience), as well as quantum communication and information theory.