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
- Learning from experts, multiplicative weights.
- Matrix completion.
- â„“1/â„“1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- P-stable sketch analysis, Nisan's PRG, â„“p estimation for p
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 19
Analysis of â„“p estimation Krahmer-Ward proof, Iterative Hard Thresholding. Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Distinct elements, k-wise independence, geometric subsampling of streams.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 19.
