Understanding Algorithms For Big Data Compsci 229r Lecture 4
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 4, you have come to the right place. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 4
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 4
Analysis of ℓp estimation Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Competitive paging, cache-oblivious
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