Media Summary: RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem. Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Krahmer-Ward proof, Iterative Hard Thresholding.
Algorithms For Big Data Compsci 229r Lecture 19 - Detailed Analysis & Overview
RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem. Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Krahmer-Ward proof, Iterative Hard Thresholding. ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Learning from experts, multiplicative weights.
