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Exploring Linear Discriminant Analysis Classification of Non-Normal Data using Poker Hands Nicholas Zuber and Lucas Tata University of Massachusetts Lowell {nicholas zuber, lucas tata}@student.uml.edu Abstract. Using linear discriminant analysis (LDA) as a linear classifier can produce well behaved results when the assumptions of this method ...

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線形判別分析(Linear Discriminant Analysis, LDA) • 1次元(z)に線形写像し、z で2 つのクラスを識別する • 2つのクラスを“最もよく判別する”ように線形写像する • クラスが3つ以上あるときにも拡張できる 1 z w x w x= + 1 1 2 2 x1 x2 z クラス 1 クラス -1

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statistical methods. This thesis concerns contributions to linear discriminant analysis with applications to growth gurves. 1.1 Background In the late 1920’s and in the 1930’s multivariate statistics was developing and attracted researchers. The first one to deal with what today is known as discriminant analysis was Fisher (1936).

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Discriminant function analysis (da). John Poulsen and Aaron French. Discriminant function analysis is multivariate analysis of variance (MANOVA) reversed. Classification functions are derived from the linear discriminant functions to achieve this purpose.

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Linear Discriminant Analysis in R Steps Prerequisites require(MASS) Model Fit the model ldaModel=lda(Target~Variable1+Variable2,data It plots a linear discriminant function separately, the values of the linear discriminant function, separately for the up group and the down group.

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A new nonmetric linear discriminant analysis approach is proposed that is based on the maximization of an index of separation differing from that used by the classical method. The possibility of choosing between Fisher's classical discriminant function and the one proposed here enables us to reduce the number of misclassifications for given data. classes become linear if the classes have a shared covariance matrix. In this case the decision surfaces are called Fisher discriminants, and the procedure of constructing them is called Linear Discriminant Analysis [10, 2]. The second use of the term LDA refers to a discriminative feature transform that is optimal for certain cases [10].

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