Low-rank approximation is a mathematical technique used for data compression and modeling, which involves minimizing the difference between a given matrix and an approximating matrix with reduced rank. It is related to other techniques such as principal component analysis and factor analysis.
UC Berkeley
Fall 2013
This course investigates the mathematical principles behind data and information analysis. It brings together concepts from statistics, optimization, and computer science, with a focus on large deviation inequalities, and convex analysis. It's tailored towards advanced graduate students who wish to incorporate these theories into their research.
No concepts data
+ 20 more concepts