Paper Available: The canonical metric in vector quantization

[Jon Baxter]

The following paper is available via anonymous ftp from 
calvin.maths.flinders.edu.au:://pub/jon/quant.ps.Z
FTP instructions are given at the end of the message.

Title: The Canonical Metric For Vector Quantization, 8 pages.
Author: Jonathan Baxter
Abstract:
To measure the quality of a set of vector quantization points a means of
measuring the distance between two points is required. Common metrics such
as the {\em Hamming} and {\em Euclidean} metrics, while mathematically
simple, are inappropriate for comparing speech signals or images.
In this paper it is argued that there often exists
a natural {\em environment} of functions to the quantization process (for
example, the word classifiers in speech recognition and the character
classifiers in character recognition) and that such an enviroment induces a
{\em canonical metric} on the space being quantized.
It is proved that optimizing the {\em reconstruction error} with respect to the
canonical metric gives rise to optimal approximations of the functions in the
environment, so that the canonical metric can be viewed as embodying
all the essential information relevant to learning the functions in the
environment. Techniques for {\em learning} the canonical metric are
discussed, in particular the relationship between learning the canonical
metric and {\em internal representation learning}.


FTP Instructions:

unix> ftp calvin.maths.flinders.edu.au (or 129.96.32.2)
  login: anonymous
  password: (your e-mail address)
ftp> cd pub/jon
ftp> binary
ftp> get quant.ps.Z
ftp> quit
unix> uncompress quant.ps.Z
unix> lpr quant.ps (or however you print)