https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
The Marchenko-Pastur distribution derives from random matrix theory a nice theoretical border to estimate if a principal component is more noise then data.
Also I am a huge fan of all sorts of embedding/projection/matrix factorization algorithms and I use them quite regularly.
Connect a huge number of graph isomorphism algorithms with a algebra.
Best First Upper Confidence Bound Tree Search.
Monte Carlo Counterfactual Regret Minimization.
Temporally Weighted Averaging - eg discount the first ten samples.
Markov Chain Monte Carlo Sampling
Unification
Definitely changed the way I think about using BFS/DFS to find paths.
* Levenshtein
* Jaccard
* Cosine
* Jaro–Winkler
* Soundex
and many many more!