By Kardi Teknomo, PhD .

Linear Discriminant Analysis (LDA) Tutorial

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What is Linear Discriminant Analysis (LDA)?

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 pattern recognition , supervised learning , or supervised classification . 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). You can download the MS Excel worksheet companion of this tutorial here


Click on the following topics below

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 .

Preferable reference for this tutorial is

Teknomo, Kardi (2019) Discriminant Analysis Tutorial. tutorial/LDA/