A sparse support vector machine classifier with nonparametric discriminants
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Non-parametric Fisher's discriminant analysis with kernels for data classification
Pattern Recognition Letters
Structure preserving non-negative matrix factorization for dimensionality reduction
Computer Vision and Image Understanding
Salient and non-salient fiducial detection using a probabilistic graphical model
Pattern Recognition
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
| Hi-index | 0.14 |
Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separable problem to a space of intrinsically larger dimensionality where the classes are linearly separable. A major problem in the design of kernel methods is to find the kernel parameters that make the problem linear in the mapped representation. This paper derives the first criterion that specifically aims to find a kernel representation where the Bayes classifier becomes linear. We illustrate how this result can be successfully applied in several kernel discriminant analysis algorithms. Experimental results, using a large number of databases and classifiers, demonstrate the utility of the proposed approach. The paper also shows (theoretically and experimentally) that a kernel version of Subclass Discriminant Analysis yields the highest recognition rates.