Why Do We Use Linear Discriminant Analysis

Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template.

What is LDA when do you use it?

It is used as a pre-processing step in Machine Learning and applications of pattern classification. 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.

What are the assumptions of LDA?

LDA makes some simplifying assumptions about your data: That your data is Gaussian, that each variable is is shaped like a bell curve when plotted. That each attribute has the same variance, that values of each variable vary around the mean by the same amount on average.