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
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Matrix completion.
- CountSketch, ℓ0 sampling, graph sketching.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- ORS theorem (distributional JL implies Gordon's theorem), sparse JL.
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.
Analysis of ℓp estimation
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 8.
