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
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Analysis of ℓp estimation External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Matrix completion.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 17
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- Path-following interior point, first order methods (gradient descent).
- Amnesic dynamic programming (approximate distance to monotonicity).
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