no code implementations • 11 May 2023 • Timothy Chu, Gary Miller, Noel Walkington
We provide theoretically-informed intuition about spectral clustering on large data sets drawn from probability densities, by proving when a continuous form of spectral clustering considered by past researchers (the unweighted spectral cut of a probability density) finds good clusters of the underlying density itself.
no code implementations • 23 Nov 2020 • Josh Alman, Timothy Chu, Gary Miller, Shyam Narayanan, Mark Sellke, Zhao Song
This completes the theory of Manhattan to Manhattan metric transforms initiated by Assouad in 1980.