Lecture

Non-Negative Definite Matrices and Covariance Matrices

Description

This lecture covers the definitions of non-negative definite and positive definite matrices, as well as the covariance matrices. It explains how to determine if a matrix is non-negative definite based on quadratic form and spectral definitions. The lecture also discusses the covariance matrix of a random vector, encoding variances and covariances. Additionally, it explores the relationship between non-negative definite matrices and covariance matrices, showing that a real symmetric matrix is non-negative definite if and only if it is the covariance matrix of some random variable. Principal Component Analysis is introduced as a method for optimal linear dimension reduction, emphasizing the importance of choosing the top eigenvectors of the covariance matrix.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.