Title
Geometric interpretation of admissible linear decision boundaries for two multivariate normal distributions (Corresp.)
Abstract
Every admissible linear decision boundary for the two-class multivariate normal recognition problem is known to be a hyperplane that is tangent to two tangent ellipsoids at their point of tangency. The ellipsoids are equiprobability surfaces for the distributions describing the classes. In this correspondence, the locus of tangent points is parameterized in a manner similar to that of Clunies-Ross and Riffenburgh.^1Anderson and Bahadur's work2 is then used to indicate which points on the locus give rise to admissible linear decision boundaries. A simple geometric proof is given for the characterization of admissible linear decision boundaries as tangent hyperplanes.
Year
DOI
Venue
1971
10.1109/TIT.1971.1054714
Information Theory, IEEE Transactions  
Keywords
Field
DocType
Pattern classification
Discrete mathematics,Combinatorics,Parameterized complexity,Ellipsoid,Equiprobability,Tangent,Multivariate normal distribution,Covariance matrix,Hyperplane,Decision boundary,Mathematics
Journal
Volume
Issue
ISSN
17
6
0018-9448
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Bechtel, F.100.34
Gavin, W.200.34
Bachand, G.300.34