Abstract
A nonsingular transformation of binary-valued random vectors y equals xA which minimizes a mutual information criterion I(y) is considered. It is shown that a nonsingular A exists such that I(y) equals 0 if and only if x has a generalized binomial distribution. Computational algorithms for seeking an optimal A are developed, and dimensionality reduction is discussed briefly. This linear transformation is useful in improving the approximation of probability distributions. Numerical examples are presented.
| Original language | English |
|---|---|
| Pages (from-to) | 152-156 |
| Number of pages | 5 |
| Journal | IEEE Transactions on Information Theory |
| Volume | IT-24 |
| Issue number | 2 |
| State | Published - Mar 1 1978 |
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ASJC Scopus subject areas
- Information Systems
- Electrical and Electronic Engineering
Cite this
LINEAR TRANSFORMATION OF BINARY RANDOM VECTORS AND ITS APPLICATION TO APPROXIMATING PROBABILITY DISTRIBUTIONS. / Young, Tzay Y.; Liu, Philip S.
In: IEEE Transactions on Information Theory, Vol. IT-24, No. 2, 01.03.1978, p. 152-156.Research output: Contribution to journal › Article
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TY - JOUR
T1 - LINEAR TRANSFORMATION OF BINARY RANDOM VECTORS AND ITS APPLICATION TO APPROXIMATING PROBABILITY DISTRIBUTIONS.
AU - Young, Tzay Y.
AU - Liu, Philip S.
PY - 1978/3/1
Y1 - 1978/3/1
N2 - A nonsingular transformation of binary-valued random vectors y equals xA which minimizes a mutual information criterion I(y) is considered. It is shown that a nonsingular A exists such that I(y) equals 0 if and only if x has a generalized binomial distribution. Computational algorithms for seeking an optimal A are developed, and dimensionality reduction is discussed briefly. This linear transformation is useful in improving the approximation of probability distributions. Numerical examples are presented.
AB - A nonsingular transformation of binary-valued random vectors y equals xA which minimizes a mutual information criterion I(y) is considered. It is shown that a nonsingular A exists such that I(y) equals 0 if and only if x has a generalized binomial distribution. Computational algorithms for seeking an optimal A are developed, and dimensionality reduction is discussed briefly. This linear transformation is useful in improving the approximation of probability distributions. Numerical examples are presented.
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UR - http://www.scopus.com/inward/citedby.url?scp=0017946770&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0017946770
VL - IT-24
SP - 152
EP - 156
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
SN - 0018-9448
IS - 2
ER -