Multivariate analysis: Classification and discriminant analysis · Advanced reference manual · Introduction

Introduction

What this guide covers

Multivariate analysis addresses the statistical study of units on which a vector of simultaneous observations is measured. Unlike conventional univariate analysis, the multivariate framework assumes that internal covariation between variables has critical information for decision making.

Overview

Topics covered

Introduction
Classification and Discriminant Analysis
Fundamentals of Multivariate Analysis and Taxonomy of Classification
The Geometry of Multivariate Space
Taxonomy: Supervised vs. Unsupervised Learning
Bayesian Decision Theory and Classification Rules

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

D_M(\mathbf{x}, \mathbf{y}) = \sqrt{(\mathbf{x} - \mathbf{y})^{\top}\boldsymbol{\Sigma}^{-1}(\mathbf{x} - \mathbf{y})}

View LaTeX source

D_M(\mathbf{x}, \mathbf{y}) = \sqrt{(\mathbf{x} - \mathbf{y})^{\top}\boldsymbol{\Sigma}^{-1}(\mathbf{x} - \mathbf{y})}