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Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or ...
In statistics, kernel Fisher discriminant analysis (KFD), [1] also known as generalized discriminant analysis [2] and kernel discriminant analysis, [3] is a kernelized version of linear discriminant analysis (LDA). It is named after Ronald Fisher.
The use of discriminant function analysis in FORDISC allows the user to sort individuals into specific groups that are defined by certain criteria. The discriminate function analysis "analyzes specific groups with known membership in discrete categories such as ancestry, language, sex, tribe or ancestry, and provides a basis for the ...
Discriminant analysis, or canonical variate analysis, attempts to establish whether a set of variables can be used to distinguish between two or more groups of cases. Linear discriminant analysis (LDA) computes a linear predictor from two sets of normally distributed data to allow for classification of new observations.
During the process of extracting the discriminative features prior to the clustering, Principal component analysis (PCA), though commonly used, is not a necessarily discriminative approach. In contrast, LDA is a discriminative one. [9] Linear discriminant analysis (LDA), provides an efficient way of eliminating the disadvantage we list above ...
Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces.
Early work on statistical classification was undertaken by Fisher, [1] [2] in the context of two-group problems, leading to Fisher's linear discriminant function as the rule for assigning a group to a new observation. [3] This early work assumed that data-values within each of the two groups had a multivariate normal distribution.
Optimal discriminant analysis may be applied to > 0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. Optimal discriminant analysis is an alternative to ANOVA (analysis of variance) and regression analysis.