Understanding Algorithms For Big Data Compsci 229r Lecture 8
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 8. Amnesic dynamic programming (approximate distance to monotonicity).
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 8
- Matrix completion.
- CountSketch, ℓ0 sampling, graph sketching.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Competitive paging, cache-oblivious
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 8
Online External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
ORS theorem (distributional JL implies Gordon's theorem), sparse JL.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 8.