Exploring Algorithms For Big Data Compsci 229r Lecture 15
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- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 15
Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. Distinct elements, k-wise independence, geometric subsampling of streams. linear programming: standard form, vertices, bases, simplex. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
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