An Optimal Set of Discriminant Vectors

IEEE Transactions on ComputersPublished 1 March 1975

Donald H. Foley, John W. Sammon

DOI10.1109/t-c.1975.224208

Source URL

Citations404

SJR quartileQ1

SJR score1.16

SNIP1.61

TL;DR

A new method for the extraction of features in a two-class pattern recognition problem is derived that is based entirely upon discrimination or separability as opposed to the more common approach of fitting.

Abstract

A new method for the extraction of features in a two-class pattern recognition problem is derived. The main advantage is that the method for selecting features is based entirely upon discrimination or separability as opposed to the more common approach of fitting. The classical example of fitting is the use of the eigenvectors of the lumped covariance matrix corresponding to the largest eigenvalues. In an analogous manner, the new technique selects discriminant vectors (or features) corresponding to the largest "discrim-values." The new method is compared to some of the more popular alternative techniques via both data-dependent and mathematical examples. In addition, a recursive method for obtaining the discriminant vectors is given.

Keywords

ChemistryComputer ScienceEngineering

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