Abstract
A random vector originates from one of r known normal populations having a common covariance matrix. We wish to reduce the dimension of the vector by means of a linear map from the original space down to a space of lower dimension while keeping the populations as separate as possible. The commonly used linear maps which are optimal for a class of measures of separation may be very poor in terms of a different criterion: the probability of correct classification calculated with no prior information about the population of origin.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
