MERCREDI 14 FEVRIER, 16h, AMPHI GALOIS/RATAUD

Fun with Nearest-Neighbor Quantizers

Svetlana Lazebnik (slazebni -at- uiuc.edu)
Dept. of Computer Science and Beckman Institute
University of Illinois at Urbana-Champaign

http://www-cvr.ai.uiuc.edu/~slazebni/research/

Abstract: I will present recent research on using nearest-neighbor vector 
quantization for estimating intrinsic dimensionality of high-dimensional 
datasets and for learning informative partitions of labeled data.

In the first part of the talk, I will discuss a technique for intrinsic
dimensionality estimation based on the theoretical notion of quantization 
dimension. This technique works by quantizing the dataset at increasing 
rates (in practice, we use k-means to learn the quantizer) and by fitting
a parametric form to the plot of the empirical quantizer distortion
as a function of rate. By using tree-structured quantization, we can 
simultaneously estimate dimensionality and partition the dataset into 
subsets having different intrinsic dimensions. 

In the second part of the talk, I will discuss an information-theoretic 
method for learning a nearest-neighbor quantizer from labeled continuous 
data such that the index of the nearest prototype of a given data point 
approximates a sufficient statistic for its class label. I will 
demonstrate applications of this method to learning discriminative
visual vocabularies for bag-of-features image classification and to
image segmentation.

This is joint work with Maxim Raginsky.