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.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Matrix completion.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 19
Learning from experts, multiplicative weights. Krahmer-Ward proof, Iterative Hard Thresholding. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 19.