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Quadratic discriminant analysis (QDA) is closely related to linear discriminant analysis (LDA), where it is assumed that the measurements from each class are normally distributed. [1] Unlike LDA however, in QDA there is no assumption that the covariance of each of the classes is identical. [2]
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 ...
The probability content of the multivariate normal in a quadratic domain defined by () = ′ + ′ + > (where is a matrix, is a vector, and is a scalar), which is relevant for Bayesian classification/decision theory using Gaussian discriminant analysis, is given by the generalized chi-squared distribution. [17]
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Quadratic discriminant analysis as used in statistical classification or as a quadratic classifier in machine learning The .QDA filename extension, used for Quadruple D archives Topics referred to by the same term
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The discriminant of a quadratic form is invariant under linear changes of variables (that is a change of basis of the vector space on which the quadratic form is defined) in the following sense: a linear change of variables is defined by a nonsingular matrix S, changes the matrix A into , and thus multiplies the discriminant by the square of ...
Classifying normal vectors using Gaussian discriminant analysis [ edit ] If x {\displaystyle {\boldsymbol {x}}} is a normal vector, its log likelihood is a quadratic form of x {\displaystyle {\boldsymbol {x}}} , and is hence distributed as a generalized chi-squared.