Algorithms for Big Data (COMPSCI 229r), Lecture 12

Algorithms for Big Data (COMPSCI 229r), Lecture 12 This lecture explores lower bounds for the Johnson-Lindenstrauss lemma, distinguishing between distributional and metric versions. It introduces Gordon's theorem as a method for dimensionality reduction on infinite sets, shifting focus from worst-case analysis to instance-specific geometric properties of vector databases