Machine learning models are created using linear discriminant analysis, a supervised classification method. These dimensionality reduction methods are employed in a variety of applications, including marketing predictive analysis and picture identification.
Before classification, linear discriminant analysis is performed to reduce the number of features to a more manageable quantity. Each of the additional dimensions is a template made up of a linear combination of pixel values.
The goal of LDA is to use a linear discriminant function to maximise between-class variance and reduce within-class variance under the assumption that data in each class is characterised by a Gaussian probability density function with the same covariance.