By Topic

Recursive partitioning to reduce distortion

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Nobel, A.B. ; Dept. of Stat., North Carolina Univ., Chapel Hill, NC, USA

Adaptive partitioning of a multidimensional feature space plays a fundamental role in the design of data-compression schemes. Most partition-based design methods operate in an iterative fashion, seeking to reduce distortion at each stage of their operation by implementing a linear split of a selected cell. The operation and eventual outcome of such methods is easily described in terms of binary tree-structured vector quantizers. This paper considers a class of simple growing procedures for tree-structured vector quantizers. Of primary interest is the asymptotic distortion of quantizers produced by the unsupervised implementation of the procedures. It is shown that application of the procedures to a convergent sequence of distributions with a suitable limit yields quantizers whose distortion tends to zero. Analogous results are established for tree-structured vector quantizers produced from stationary ergodic training data. The analysis is applicable to procedures employing both axis-parallel and oblique splitting, and a variety of distortion measures. The results of the paper apply directly to unsupervised procedures that may be efficiently implemented on a digital computer

Published in:

Information Theory, IEEE Transactions on  (Volume:43 ,  Issue: 4 )