Abstract
Linear discriminant analysis (LDA) is one of the well known methods to extract the best features for multi-class discrimination. Recently Kernel discriminant analysis (KDA) has been successfully applied in many applications. KDA is one of the nonlinear extensions of LDA and construct nonlinear discriminant mapping by using kernel functions. But the kernel function is usually defined a priori and it is not known what the optimum kernel function for nonlinear discriminant analysis is. Also the class information is not usually introduced to define the kernel functions. In this paper the optimum kernel function in terms of the discriminant criterion is derived by investigating the optimum discriminant mapping constructed by the optimum nonlinear discriminant analysis (ONDA).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.