Exploring Algorithms For Big Data Compsci 229r Lecture 18
Welcome to our comprehensive guide on Algorithms For Big Data Compsci 229r Lecture 18.
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
- Krahmer-Ward proof, Iterative Hard Thresholding.
- So, now we have finished 2 weeks in this course on
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- Organizers: Ehsan Elhamifar Amit Roy-Chowdhury Amin Karbasi Description: The increasing amounts of
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.
Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
In summary, understanding Algorithms For Big Data Compsci 229r Lecture 18 gives us a better perspective.