Algorithms for Big Data (COMPSCI 229r), Lecture 13

Algorithms for Big Data (COMPSCI 229r), Lecture 13 This lecture explores instance-wise dimensionality reduction beyond worst-case analysis. It covers the proof of Gordon's theorem as an extension of the Johnson-Lindenstrauss lemma and discusses techniques for accelerating the computation of dimensionality reduction, including the use of sparse matrices to improve performance while maintaining desired accuracy guarantees.