Exploring Algorithms For Big Data Compsci 229r Lecture 15
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 15, you have come to the right place.
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Lecture 15
- Krahmer-Ward proof, Iterative Hard Thresholding.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Matrix completion.
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 15
Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. 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 ... Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 15 was helpful.