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 | |
| 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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Cite this
Young, T. Y., & Liu, P. S. (1981). Statistical Pattern Classification with Binary Variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-3(2), 155-163. https://doi.org/10.1109/TPAMI.1981.4767073