Explore the robust variant of principal component analysis (RPCA) in this 22-minute video lecture. Learn how robust statistics handle data with corruption or missing entries, making RPCA a crucial algorithm in fields like fluid mechanics, the Netflix prize, and image processing. Discover the basic problem, motivation, and core ideas behind RPCA. Examine its applications in fluid flows and the Netflix Prize challenge. Access additional resources, including the companion book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz, for a deeper understanding of the topic.
Overview
Syllabus
Introduction
Basic Problem
Motivation
Basic Idea
Fluid Flows
Netflix Prize
Taught by
Steve Brunton
