Advanced Techniques for Low-Rank Matrix Approximation
Offered By: Simons Institute via YouTube
Course Description
Overview
Explore advanced techniques for low-rank matrix approximation in this 32-minute lecture by Ming Gu from UC Berkeley. Delve into randomized numerical linear algebra and its applications, covering topics such as acceptance rates, traditional approaches, factorizations, the Rockman method, and comparisons of quality versus time. Examine block size considerations and uniqueness in matrix approximation, gaining insights into cutting-edge methods for efficient data analysis and computation.
Syllabus
Intro
Outline
Acceptance Rates
What is going on
Traditional Approach
Factorizations
Rockman
Method
Comparison
Quality vs Time
Block Size
Uniqueness
Taught by
Simons Institute
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