title: Feature extraction using problem localization.
abstract: Feature extraction is considered as a mean-quare estimation of the Bayes risk vector. The problem is simplified by partitioning the distribution space into local subregions and performing a linear estimation in each subregion. A modified clustering algorithm is used to fimd the partitioning which minimizes the mean-square error.
authors.affiliation: Sperry Research Center, Sudbury, MA 01776.
authors.forename: R D
authors.initials: RD
authors.lastname: Short
authors.forename: K
authors.initials: K
authors.lastname: Fukunaga