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
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Competitive paging, cache-oblivious
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 16
Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
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