Understanding Algorithms For Big Data Compsci 229r Lecture 19
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 19. RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 19
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- Learning from experts, multiplicative weights.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 19
Matrix completion. Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Krahmer-Ward proof, Iterative Hard Thresholding.
Competitive paging, cache-oblivious
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 19.
