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linear discriminant analysis

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  • Linear discriminant analysis — (LDA) and the related Fisher s linear discriminant are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events. The… …   Wikipedia

  • Linear discriminant analysis — Die Diskriminanzanalyse ist eine Methode der multivariaten Verfahren in der Statistik. Sie wurde von R. A. Fisher 1936 zum ersten Mal in The use of multiple measurements in taxonomic problems[1] beschrieben. Sie wird in der Statistik und im… …   Deutsch Wikipedia

  • Optimal discriminant analysis — (ODA) and the related classification tree analysis (CTA) are statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical …   Wikipedia

  • Discriminant function analysis — is a statistical analysis to predict a categorical dependent variable by one or more continuous or binary independent variables. It is different from an ANOVA or MANOVA, which is used to predict one (ANOVA) or multiple (MANOVA) continuous… …   Wikipedia

  • Linear classifier — In the field of machine learning, the goal of classification is to group items that have similar feature values, into groups. A linear classifier achieves this by making a classification decision based on the value of the linear combination of… …   Wikipedia

  • discriminant function — Statistics. a linear function of measurements of different properties of an object or event that is used to assign the object or event to one population or another (discriminant analysis). [1935 40] * * * …   Universalium

  • Principal component analysis — PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by… …   Wikipedia

  • Principal components analysis — Principal component analysis (PCA) is a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the discrete Karhunen Loève transform (KLT),… …   Wikipedia

  • multivariate analysis — Univariate analysis consists in describing and explaining the variation in a single variable. Bivariate analysis does the same for two variables taken together (covariation). Multivariate analysis (MVA) considers the simultaneous effects of many… …   Dictionary of sociology

  • Multivariate analysis of variance — (MANOVA) is a generalized form of univariate analysis of variance (ANOVA). It is used when there are two or more dependent variables. It helps to answer : 1. do changes in the independent variable(s) have significant effects on the dependent …   Wikipedia

  • Multilinear subspace learning — (MSL) aims to learn a specific small part of a large space of multidimensional objects having a particular desired property. It is a dimensionality reduction approach for finding a low dimensional representation with certain preferred… …   Wikipedia

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