title: Kernel discriminant analysis using case-specific smoothing parameters.
abstract: In kernel discriminant analysis, one common practice is to use a fixed level of smoothing (estimated from training data) for classifying all unlabeled observations. But, in classification, a good choice of smoothing parameters also depends on the observation to be classified. Therefore, instead of using a fixed level of smoothing over the entire measurement space, it may be more useful to estimate the smoothing parameters depending on that specific observation. Here, we propose a simple method for this case-specific smoothing. Some benchmark data sets are analyzed to illustrate the performance of the proposed method.
authors.affiliation: Department of Mathematics and Statistics, IndianInstitute of Technology, Kanpur 208016, India. akghosh@isical.ac.in
authors.forename: Anil K
authors.initials: AK
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted