Introduction•goal of differentiating or discriminating the response variableinto its distinct classes.•most commonly used dimensionality reduction technique insupervised learning.•preprocessing step for pattern classification andmachine learning applications.•minimize over fitting and computational costs.•Under Linear Discriminant Analysis, we are basically lookingfor1. Which set of parameters can best describe theassociation of the group for an object?2. What is the best classification preceptor model thatseparates those groups?
Linear Discriminant analysisDiscriminant analysis is putto usewhen we already have predefinedclasses/categories of responseand wewant to build a model that helps indistinctly predicting the class, if anynew observation comes into equation.
Difference between LDA and PCA
Difference between LDA and PCA•LDA approach is very similar to Principal Component Analysis•