Abstract
A random vector originates from one of r known normal populations having a common covariance matrix. We wish to reduce the dimension of the vector by means of a linear map from the original space down to a space of lower dimension while keeping the populations as separate as possible. The commonly used linear maps which are optimal for a class of measures of separation may be very poor in terms of a different criterion: the probability of correct classification calculated with no prior information about the population of origin.
Original language | English (US) |
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Pages (from-to) | 323-330 |
Number of pages | 8 |
Journal | Journal of Statistical Planning and Inference |
Volume | 14 |
Issue number | 2-3 |
DOIs | |
State | Published - 1986 |
Externally published | Yes |
Keywords
- Allocation
- Linear discrimination
- Probability of correct classification
- Separation
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics