At a Glance: Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Algorithms For Big Data Compsci 229r Lecture 17 -
Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
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- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
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