Low Rank Tensor Methods in High Dimensional Data Analysis

Explore the first part of a comprehensive lecture on low rank tensor methods in high dimensional data analysis, delivered by Ming Yuan from Columbia University's Statistics department. Delve into the challenges and recent advancements in analyzing multidimensional data in the form of tensors, which are increasingly prevalent in fields such as chemometrics, genomics, physics, psychology, and signal processing. Gain insights into the growing need for innovative statistical methods, efficient computational algorithms, and fundamental mathematical theory to effectively extract useful information from these complex data structures. Learn about the current state of tensor analysis and the obstacles faced in keeping pace with the rapid generation and acquisition of multidimensional data in modern applications.

Ming Yuan: "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 1/2)"