Quantum Clustering
David Horn, Assaf Gottlieb
Abstract
We propose a novel clustering method that is based on physical intuition derived from quantum mechanics. Starting with given data points, we construct a scale-space probability function. Viewing the latter as the lowest eigenstate of a Schrodinger equation, we use simple analytic operations to derive a potential function whose minima determine cluster centers. The method has one parameter, determining the scale over which cluster structures are searched. We demonstrate it on data analyzed in two dimensions (chosen from the eigenvectors of the correlation matrix). The method is applicable in higher dimensions by limiting the evaluation of the Schrodinger potential to the locations of data points. In this case the method may be formulated in terms of distances between data points.
Create a lesson
Related papers
Reduced latent leakage does not reliably predict lower likelihood bias in collider inference
Tong Pan
The Greedy Bump Bias: Local Profiling Geometry and the Look-Elsewhere Effect
Tommaso Dorigo
Multi-fidelity Monte Carlo estimation of floor response spectra under combined seismic and structural parameter uncertainties
Nils Baillie, Baptiste Kerleguer, Cyril Feau et al.
Parameter inference from a non-stationary unknown process using statistical feature-based slow feature analysis
Kieran S. Owens, Masako Tamaki, Ben D. Fulcher
A Probability Model for Pentagonal Prism Dice Rolls
Paul R. Hurst, J. Naleo Hyde
Geometry-native machine learning reconstruction of DSMC moment fields with support monitoring
Ehsan Roohi