Understanding Algorithms For Big Data Compsci 229r Lecture 14
Exploring Algorithms For Big Data Compsci 229r Lecture 14 reveals several interesting facts. Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 14
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 14
Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. ORS theorem (distributional JL implies Gordon's theorem), sparse JL. Titus Brown Random
Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
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