Fisher+Kernel Criterion for Discriminant Analysis

Authors:
Shu Yang;Shuicheng Yan;Dong Xu;Xiaoou Tang;Chao Zhang
Affiliations:
Peking University;Chinese University of Hong Kong;University of Science and Technology of China;Chinese University of Hong Kong and Microsoft Research Asia;Peking University
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Year:
2005

Citing 0
Cited 3

Face recognition using a kernel fractional-step discriminant analysis algorithm

Pattern Recognition
A two-stage head pose estimation framework and evaluation

Pattern Recognition
From fisher's linear discriminant analysis to NLDA or the story of the solution of a very difficult nonlinear classification problem

SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an efficient procedure is derived to optimize this new criterion in an iterative manner. More specifically, original input vector is first transformed into a higher dimensional feature matrix through a battery of nonlinear mappings involved in different kernels. Then, based on the feature matrices, FKC is presented within two coupled projection spaces: one projection space is used to search for the optimal combinations of kernels; while the other encodes the optimal nonlinear discriminating projection directions. Our proposed method is a unified framework for both kernel selection and nonlinear discriminant analysis. Besides, the algorithm potentially alleviates overfitting problem existing in traditional KDA and has no singularity problems in most cases. The effectiveness of our proposed algorithm is validated by extensive face recognition experiments on several datasets.