Understanding Algorithms For Big Data Compsci 229r Lecture 5
Exploring Algorithms For Big Data Compsci 229r Lecture 5 reveals several interesting facts. Analysis of ℓp estimation
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 5
- CountMin sketch, point query,
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
Hashing: cuckoo hashing analysis, power of two choices. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. Amnesic dynamic programming (approximate distance to monotonicity).
CountSketch, ℓ0 sampling, graph sketching.
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