Quantum Interference for Counting Clusters
This is an incremental approach for machine learning researchers dealing with overlapping cluster analysis.
The paper tackles the problem of counting overlapping clusters in machine learning by applying a quantum theory formulated with path integrals, showing it can be a more robust statistical method for separating data, with confirmation from data simulations.
Counting the number of clusters, when these clusters overlap significantly is a challenging problem in machine learning. We argue that a purely mathematical quantum theory, formulated using the path integral technique, when applied to non-physics modeling leads to non-physics quantum theories that are statistical in nature. We show that a quantum theory can be a more robust statistical theory to separate data to count overlapping clusters. The theory is also confirmed from data simulations.This works identify how quantum theory can be effective in counting clusters and hope to inspire the field to further apply such techniques.