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
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Matrix completion.
- 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
Krahmer-Ward proof, Iterative Hard Thresholding. Amnesic dynamic programming (approximate distance to monotonicity). Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 19.
