Reference Summary: Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
Algorithms For Big Data Compsci 229r Lecture 3 -
Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Important details found
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- This is CS50, Harvard University's introduction to the intellectual enterprises of
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