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
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 19
Krahmer-Ward proof, Iterative Hard Thresholding. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 19.