Low Rank Approximations

This lecture covers the concept of low rank approximations, focusing on the mathematical theory behind it and its applications. The instructor explains the process of finding the best approximation of a matrix by a low-rank matrix, emphasizing the importance of spectral theorems and orthogonality. Various demonstrations and examples are provided to illustrate the theory, including the minimization of certain functions. The lecture concludes with a discussion on the practical implications of low rank approximations in data analysis and signal processing.