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
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- 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.
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'
Amnesic dynamic programming (approximate distance to monotonicity).
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 4 was helpful.
