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International Journal of Sensor Networks and Data Communications

International Journal of Sensor Networks and Data Communications

ISSN: 2090-4886

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Linear Discriminant Analysis

Linear discriminant analysis, or discriminating function analysis is a generalization of Fisher's linear discriminant, a method used in pattern recognition, statistics, and machine learning to find a linear combination of features which characterizes or distinguishes two or more groups of objects or events. The resulting mixture may be used as a linear classifier, or, more generally, before later classification, for reduction of dimensionality. LDA is closely related to variance analysis (ANOVA) and regression analysis, which often seeks to describe one dependent variable as a linear combination of certain characteristics or measurements. ANOVA therefore uses categorical independent variables and a continuous dependent variable, whereas selective analysis has continuous independent variables and a categorical dependent variable. Logistic regression and probit regression are more similar to LDA than ANOVA is, as the values of continuous independent variables also describe a categorical variable. In applications where it is not reasonable to assume that the independent variables are normally distributed, which is a fundamental assumption of the LDA method, those other methods are preferable.

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Citations: 343

International Journal of Sensor Networks and Data Communications received 343 citations as per Google Scholar report

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