Statistical Pattern Classification with Binary Variables

Tzay Y. Young; Philip S. Liu

doi:10.1109/TPAMI.1981.4767073

Statistical Pattern Classification with Binary Variables

Tzay Y. Young, Philip S. Liu

Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Binary random variables are regarded as random vectors in a binary-field (modulo-2) linear vector space. A characteristic function is defined and related results derived using this formulation. Minimax estimation of probability distributions using an entropy criterion is investigated, which leads to an A-distribution and bilinear discriminant functions. Nonparametric classification approaches using Hamming distances and their asymptotic properties are discussed. Experimental results are presented.

Original language	English (US)
Pages (from-to)	155-163
Number of pages	9
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	PAMI-3
Issue number	2
DOIs	https://doi.org/10.1109/TPAMI.1981.4767073
State	Published - Mar 1981

Keywords

Binary data analysis
discriminant function
minimax estimation
pattern classification
statistical analysis

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

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10.1109/TPAMI.1981.4767073

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title = "Statistical Pattern Classification with Binary Variables",

abstract = "Binary random variables are regarded as random vectors in a binary-field (modulo-2) linear vector space. A characteristic function is defined and related results derived using this formulation. Minimax estimation of probability distributions using an entropy criterion is investigated, which leads to an A-distribution and bilinear discriminant functions. Nonparametric classification approaches using Hamming distances and their asymptotic properties are discussed. Experimental results are presented.",

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AU - Young, Tzay Y.

AU - Liu, Philip S.

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AB - Binary random variables are regarded as random vectors in a binary-field (modulo-2) linear vector space. A characteristic function is defined and related results derived using this formulation. Minimax estimation of probability distributions using an entropy criterion is investigated, which leads to an A-distribution and bilinear discriminant functions. Nonparametric classification approaches using Hamming distances and their asymptotic properties are discussed. Experimental results are presented.

KW - Binary data analysis

KW - discriminant function

KW - minimax estimation

KW - pattern classification

KW - statistical analysis

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JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 2

ER -

Statistical Pattern Classification with Binary Variables

Abstract

Keywords

ASJC Scopus subject areas

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