Measuring and Decomposing Mode Separation via the Canonical Diffusion
Mode separation, namely how sharply a distribution fragments into barrier-separated clusters, is a fundamental geometric property of densities, difficult to quantify in high dimensions. It is structurally distinct from dispersion, yet existing tools fall short: differential entropy rises with spread regardless of fragmentation, PCA orders directions by variance regardless of barriers, and mutual information requires a mixture decomposition one usually does not have. We measure mode separation through a single stochastic process intrinsic to the density: a unique reversible diffusion with $f$ as its stationary distribution and constant scalar diffusion coefficient. We extract two readouts ...
| Authors | Shaul Tolkovsky, Ori Meidler, Or Zuk |
|---|---|
| Primary category | stat.ML |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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