Adaptive Nonlinear Discriminant Analysis by Regularized Minimum Squared Errors
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of generalized linear discriminant analysis algorithms
Pattern Recognition
A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A novel Bayesian logistic discriminant model: An application to face recognition
Pattern Recognition
Regularized Discriminant Analysis, Ridge Regression and Beyond
The Journal of Machine Learning Research
Robust kernel discriminant analysis using fuzzy memberships
Pattern Recognition
Speed up kernel discriminant analysis
The VLDB Journal — The International Journal on Very Large Data Bases
Computers and Industrial Engineering
Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Weighted generalized kernel discriminant analysis using fuzzy memberships
WSEAS Transactions on Mathematics
Face recognition using kernel uncorrelated discriminant analysis
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Kernel uncorrelated discriminant analysis for radar target recognition
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Extending kernel fisher discriminant analysis with the weighted pairwise chernoff criterion
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding
Neural Processing Letters
Novel Fisher discriminant classifiers
Pattern Recognition
Non-parametric Fisher's discriminant analysis with kernels for data classification
Pattern Recognition Letters
Computers and Electronics in Agriculture
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Linear discriminant analysis (LDA) has been widely used for linear dimension reduction. However, LDA has limitations in that one of the scatter matrices is required to be nonsingular and the nonlinearly clustered structure is not easily captured. In order to overcome the problems caused by the singularity of the scatter matrices, a generalization of LDA based on the generalized singular value decomposition (GSVD) was recently developed. In this paper, we propose a nonlinear discriminant analysis based on the kernel method and the GSVD. The GSVD is applied to solve the generalized eigenvalue problem which is formulated in the feature space defined by a nonlinear mapping through kernel functions. Our GSVD-based kernel discriminant analysis is theoretically compared with other kernel-based nonlinear discriminant analysis algorithms. The experimental results show that our method is an effective nonlinear dimension reduction method.