Understanding Algorithms For Big Data Compsci 229r Lecture 14
Exploring Algorithms For Big Data Compsci 229r Lecture 14 reveals several interesting facts. Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 14
- ORS theorem (distributional JL implies Gordon's theorem), sparse JL.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 14
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 ... Amnesic dynamic programming (approximate distance to monotonicity).
Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
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