Quadratic discriminant analysis by projection

Bayesian Quadratic Discriminant Analysis

Quadratic discriminant analysis is a common tool for classification, but estimation of the Gaussian parameters can be ill-posed. This paper contains theoretical and algorithmic contributions to Bayesian estimation for quadratic discriminant analysis. A distribution-based Bayesian classifier is derived using information geometry. Using a calculus of variations approach to define a functional Bre...

Classification Using Linear Discriminant Analysis and Quadratic Discriminant Analysis

2 Classification of One-Dimensional Data 2 2.1 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.1 Building the LDA Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.2 Results of One-Dimensional LDA Classification . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Quadratic Discriminant Analysis . . . . . ....

Linear and Quadratic Discriminant Analysis and Friends

Unsupervised Discriminant Projection Analysis for Feature Extraction

This paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method ocality preserving projection (LP...

Graphical Tools for Quadratic Discriminant Analysis

Sufficient dimension reduction methods provide effective ways to visualize discriminant analysis problems. For example, Cook and Yin (2001) showed that the dimension reduction method of sliced average variance estimation (save) identifies variates that are equivalent to a quadratic discriminant analysis (qda) solution. This article makes this connection explicit to motivate the use of save vari...