A model for dimension reduction in pattern recognition using continuous data

摘要

An approach to transform continuous data to finite dimensional data is briefly outlined. A model to reduce the dimension of the finite dimensional data is developed for the case when the covariance matrices are not necessarily equal. Necessary and sufficient conditions with respect to the spatial properties of the means and covariance matrices are given so that the linear transformation of data of higher dimensions to lower dimensions does not increase the probabilities of misclassification.