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
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 19
Krahmer-Ward proof, Iterative Hard Thresholding. Learning from experts, multiplicative weights. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
MapReduce: TeraSort, minimum spanning tree, triangle counting.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 19.
