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
- Hashing: cuckoo hashing analysis, power of two choices.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- CountMin sketch, point query,
- CountSketch, ℓ0 sampling, graph sketching.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
Amnesic dynamic programming (approximate distance to monotonicity). Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Matrix completion.
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