Algorithms for Big Data (COMPSCI 229r), Lecture 20

Algorithms for Big Data (COMPSCI 229r), Lecture 20 This lecture explores the theoretical connections between Restricted Isometry Property (RIP) and Distributional Johnson-Lindenstrauss (DJL) embeddings. It further examines Iterative Hard Thresholding as a computationally efficient approach for signal recovery, providing an analysis of its convergence properties and performance guarantees in high-dimensional data settings.

Algorithms for Big Data (COMPSCI 229r), Lecture 19

Algorithms for Big Data (COMPSCI 229r), Lecture 19 RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.

Algorithms for Big Data (COMPSCI 229r), Lecture 18

Algorithms for Big Data (COMPSCI 229r), Lecture 18 Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.

Algorithms for Big Data (COMPSCI 229r), Lecture 17

Algorithms for Big Data (COMPSCI 229r), Lecture 17 his lecture explores methodologies for obtaining oblivious subspace embeddings, including net arguments, non-commutative Khintchine, the moment method, and approximate matrix multiplication. It further examines the application of these techniques to improve the efficiency of least squares regression through iterative methods like gradient descent.

Algorithms for Big Data (COMPSCI 229r), Lecture 16

Algorithms for Big Data (COMPSCI 229r), Lecture 16