Main Takeaway: Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than
Algorithms For Big Data Compsci 229r Lecture 2 -
Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Important details found
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
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