A Relationship between Linear Discriminant Analysis and the Generalized Minimum Squared~Error~Solution

Authors:
Cheong Hee Park;Haesun Park
Affiliations:
-;-
Venue:
SIAM Journal on Matrix Analysis and Applications
Year:
2005

Citing 0
Cited 7

Adaptive Nonlinear Discriminant Analysis by Regularized Minimum Squared Errors

IEEE Transactions on Knowledge and Data Engineering
Least squares linear discriminant analysis

Proceedings of the 24th international conference on Machine learning
Developmental stage annotation of Drosophila gene expression pattern images via an entire solution path for LDA

ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Kernel-based nonlinear discriminant analysis using minimum squared errors criterion for multiclass and undersampled problems

Signal Processing
Regularized Discriminant Analysis, Ridge Regression and Beyond

The Journal of Machine Learning Research
A New and Fast Orthogonal Linear Discriminant Analysis on Undersampled Problems

SIAM Journal on Scientific Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a relationship between linear discriminant analysis (LDA) and the generalized minimum squared error (MSE) solution is presented. The generalized MSE solution is shown to be equivalent to applying a certain classification rule in the space defined by LDA. The relationship between the MSE solution and Fisher discriminant analysis is extended to multiclass problems and also to undersampled problems for which the classical LDA is not applicable due to singularity of the scatter matrices. In addition, an efficient algorithm for LDA is proposed exploiting its relationship with the MSE procedure. Extensive experiments verify the theoretical results.