SOURCE-LINKED INTELLIGENCE

Kinks vs. Smoothness: Identifiability of Real Analytic nICA for Laplace-like Sources

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Many machine learning systems try to explain complex data - like images or financial time series - in terms of hidden, independent factors that generated them. Recovering the true underlying factors, rather than some scrambled version of them, is the central challenge of nonlinear Independent Component Analysis (nICA). We prove identifiability (exact recovery) up to trivial ambiguities for real analytic generating functions when source probability density functions have a finite number of discontinuities in the first derivative. The Laplace distribution is the most prominent example satisfying

Read original source ↗ Open in workspace

recordType

paper

region

Global