Numerical Integration on Graphs: where to sample and how to weigh

no code implementations • 19 Mar 2018 • George C. Linderman, Stefan Steinerberger

Let $G=(V, E, w)$ be a finite, connected graph with weighted edges.

Numerical Integration

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

8 code implementations • 25 Dec 2017 • George C. Linderman, Manas Rachh, Jeremy G. Hoskins, Stefan Steinerberger, Yuval Kluger

t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years.

Dimensionality Reduction

Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science

3 code implementations • 13 Nov 2017 • George C. Linderman, Gal Mishne, Yuval Kluger, Stefan Steinerberger

If we pick $n$ random points uniformly in $[0, 1]^d$ and connect each point to its $k-$nearest neighbors, then it is well known that there exists a giant connected component with high probability.

Clustering with t-SNE, provably

2 code implementations • 8 Jun 2017 • George C. Linderman, Stefan Steinerberger

t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and visualization method proposed by van der Maaten & Hinton in 2008, has rapidly become a standard tool in a number of natural sciences.

Clustering