Discriminant analysis on land grading

Discriminant analysis on functional data

On Model-Based Clustering, Classification, and Discriminant Analysis

The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attenti...

Review on Heteroscedastic Discriminant Analysis

Discriminant feature spaces are attractive way to improve the word error rate performance of the speech recognition systems. Heteroscedastic discriminant analysis (HDA) is a generalized method for the feature space transformation that does not impose the equa l w i th in c l a s s cova r i ance assumptions required by the standard linear discriminant analysis (LDA). It will be shown that the co...

Influence Analysis on Discriminant Coordinates

Discriminant analysis (DA), particularly Discriminant Coordinates (DC), is broadly applied in the scientific literature and included in many statistical software packages. DC is used to analyze biomedical data, especially for differential diagnosis on the basis of laboratory profiles. Papers handling influence analysis in DA can be found in the literature; however this topic has been scarcely t...

A comment on "Laplacian linear discriminant analysis"

The recent paper by Tang et al. [1] has proven that Fisher’s criterion function is equal to a novel linear discriminant criterion function. The novel function is then related to spectral decomposition of the Laplacian of a graph. The equivalence between the two functions is based on Theorem 1 given in the beginning of Ref. [1]. Although some mathematical analysis is established in their paper, ...