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Discriminant analysis is a statistical technique to classify objects into mutually exclusive and exhaustive groups based on a set of measurable object's features. Term discriminant analysis comes with many different names for difference field of study. It is also often called
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pattern recognition
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,
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supervised learning
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, or
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supervised classification
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. This tutorial gives overview about Linear Discriminant Analysis (LDA). If the number of classes is more than two, it is also sometimes called Multiple Discriminant Analysis (MDA).
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You can download the MS Excel worksheet companion of this tutorial here
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Click on the following topics below
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Purpose of discriminant Analysis

Linear Discriminant Analysis (LDA)

LDA formula (and Derivation of LDA formula )

Numerical example

Difference of Cluster Analysis and Discriminant Analysis

Example of Applications

Resources on Discriminant Analysis

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See Also:

Similarity and dissimilarity measurement
(multivariate distance),
K means clustering
,
K nearest neighbor algorithm
,
Clustering
,
Decision Tree
,
Linear Algebra

This tutorial is copyrighted .

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Preferable reference for this tutorial is
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Teknomo, Kardi (2015) Discriminant Analysis Tutorial. http://people.revoledu.com/kardi/ tutorial/LDA/