Exploring Algorithms For Big Data Compsci 229r Lecture 18
Welcome to our comprehensive guide on Algorithms For Big Data Compsci 229r Lecture 18.
- second order methods (Newton's method), path-following interior point wrap-up.
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- 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.
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 18
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
Matrix completion.
In summary, understanding Algorithms For Big Data Compsci 229r Lecture 18 gives us a better perspective.