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What is Linear Discriminant Analysis ?
This algorithm can be used for both classification and dimensionality reduction. But It is mostly used for dimensionality reduction methods. This method is used in machine learning. Most commonly used for feature extraction in pattern classification problems. The goal of LDA is to project the features in higher dimensional space onto a lower-dimensional space in order to avoid the curse of dimensionality and also reduce resources and dimensional costs. Discriminant function analysis is useful in determining whether a set of variables is effective in predicting category membership.
LDA works when the measurements made on independent variables for each observation are continuous quantities. When dealing with categorical independent variables, the equivalent technique is discriminant correspondence analysis.
Why use LDA ?
Linear discriminant analysis algorithm used as a data processing technique to reduce the feature like principal component analysis algorithm.
It is used in face detection methods. It extracts useful information from the different faces.
Linear discriminant analysis handled multiple classification problems efficiently.
How does it Work
LDA algorithms focus on features in higher dimension space to lower dimensions.
It calculates the separability between classes which is the distance between the mean of different classes. This is called the between-class variance.
After the above calculation it will calculate the distance between the mean and sample of each class. It is also called the within-class variance.
At last the lower-dimensional space which maximizes the between-class variance and minimizes the within-class variance. P is considered as the lower-dimensional space projection
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