Exploring Algorithms For Big Data Compsci 229r Lecture 15
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- Lecture 15
- To follow along with the course, visit the course website: https://web.stanford.edu/class/archive/cs/cs109/cs109.1232/ Chris Piech ...
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- 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. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... 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.
ORS theorem (distributional JL implies Gordon's theorem), sparse JL.
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