Theory and practice of vector quantizers trained on small trainingsets
Cohn, D.
Riskin, E.A.
Ladner, R.
Dept. of Comput. Sci. & Eng., Washington Univ., Seattle, WA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jan 1994
Volume: 16,
Issue: 1
On page(s): 54-65
ISSN: 0162-8828
References Cited: 20
CODEN: ITPIDJ
INSPEC Accession Number: 4645053
Digital Object Identifier: 10.1109/34.273717
Current Version Published: 2002-08-06
Abstract
Examines how the performance of a memoryless vector quantizer
changes as a function of its training set size. Specifically, the
authors study how well the training set distortion predicts test
distortion when the training set is a randomly drawn subset of blocks
from the test or training image(s). Using the Vapnik-Chervonenkis (VC)
dimension, the authors derive formal bounds for the difference of test
and training distortion of vector quantizer codebooks. The authors then
describe extensive empirical simulations that test these bounds for a
variety of codebook sizes and vector dimensions, and give practical
suggestions for determining the training set size necessary to achieve
good generalization from a codebook. The authors conclude that, by using
training sets comprising only a small fraction of the available data,
one can produce results that are close to the results obtainable when
all available data are used
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