Introduction to Algorithms For Big Data Compsci 229r Lecture 6
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Algorithms For Big Data Compsci 229r Lecture 6 Comprehensive Overview
Amnesic dynamic programming (approximate distance to monotonicity). CountSketch, ℓ0 sampling, graph sketching. Analysis of ℓp estimation
Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 6
- Amortized analysis, binomial heaps, Fibonacci heaps.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Krahmer-Ward proof, Iterative Hard Thresholding.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 6 was helpful.
