Linear Discriminant Analysis

This article talks about Linear Discriminant Analysis (LDA), which is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. LDA is closely related to analysis of variance (ANOVA) and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements. LDA is also closely related to principal component analysis and factor analysis in that they both look for linear combinations of variables which best explain the data. It explicitly attempts to model the difference between the classes of data.