Understanding Algorithms For Big Data Compsci 229r Lecture 4
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 4, you have come to the right place. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 4
- Symmetrization, hashing: linear probing (5-wise indep.), bloom filters, cuckoo hashing, bloomier filters.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Hashing: cuckoo hashing analysis, power of two choices.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 4
Amnesic dynamic programming (approximate distance to monotonicity). Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... Analysis of ℓp estimation
RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 4 was helpful.
