Understanding Algorithms For Big Data Compsci 229r Lecture 16
Exploring Algorithms For Big Data Compsci 229r Lecture 16 reveals several interesting facts. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 16
- So, now we have finished 2 weeks in this course on
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma).
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 16
Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
Krahmer-Ward proof, Iterative Hard Thresholding.
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