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
- Titus Brown Random
- 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 ...
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Competitive paging, cache-oblivious
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. Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
MapReduce: TeraSort, minimum spanning tree, triangle counting.
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