Introduction to Algorithms For Big Data Compsci 229r Lecture 17
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 17, you have come to the right place. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Algorithms For Big Data Compsci 229r Lecture 17 Comprehensive Overview
Amnesic dynamic programming (approximate distance to monotonicity). Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Path-following interior point, first order methods (gradient descent).
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 17
- Analysis of ℓp estimation
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
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