Introduction to Algorithms For Big Data Compsci 229r Lecture 21
Exploring Algorithms For Big Data Compsci 229r Lecture 21 reveals several interesting facts. ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
Algorithms For Big Data Compsci 229r Lecture 21 Comprehensive Overview
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Matrix completion.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 21
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Krahmer-Ward proof, Iterative Hard Thresholding.
- Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma).
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
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