Title :
Discriminatory dimensionality reduction (Corresp.)
Author :
Kulikowski, Casimir
fDate :
7/1/1971 12:00:00 AM
Abstract :
Truncated optimal entropy-minimizing expansions can serve to characterize classes of multivariate data. A method is presented here by which the level of truncation and the corresponding dimensionalities of the class subspaces can be chosen to ensure adequate discrimination. The subspaces are chosen to maximize the average margin of correct classification of the paradigms of one class subject to constraints on the other margins.
Keywords :
Karhunen-Loeve transforms; Pattern recognition; Eigenvalues and eigenfunctions; Jacobian matrices; Notice of Violation; Pattern recognition; Probability distribution; Random processes; Signal detection; Subspace constraints;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.1971.1054648