Discriminant Analysis plays a significant role in pattern recognition, analysis of variances, and adequacy of classification. It is further used for cluster analysis
It generally classified as - Linear Discriminant Analysis, Multiple Discriminant Analysis (Factor and Canonical Discriminant Analysis) and K-NNs Discriminant Analysis.
For beginners, LDA is the more preferred analysis methodology.
The main purpose of a discriminant function analysis is to predict group membership based on a linear combination of the interval variables. The procedure begins with a set of observations where both group membership and the values of the interval variables are known.
The end result of the procedure is a model that allows prediction of group membership when only the interval variables are known. A second purpose of discriminant function analysis is an understanding of the data set, as a careful examination of the prediction model that results from the procedure can give insight into the relationship between group membership and the variables used to predict group membership.
An Example of LDA would be- a graduate admissions committee might divide a set of past graduate students into two groups: students who finished the program in five years or less and those who did not. Discriminant function analysis could be used to predict successful completion of the graduate program based on GRE score and undergraduate grade point average. Examination of the prediction model might provide insights into how each predictor individually and in combination predicted completion or non-completion of a graduate program.
The lectures that we have had as of now focused on 2 variable, LDA. Hence it would be not so appropriate to dwell into the explanation of MDA or Kernel Discriminant Analysis.
Written by: Umang Arora