Linear Independent Component Analysis via Optimal Transport
Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures. To achieve this, classical ICA algorithms attempt to maximize non-Gaussianity, measured by negentropy, which is linked to independence by information theory. Because exact negentropy optimization is intractable, they rely on proxy contrast functions, such as fourth-order cumulants and parametric log-likelihoods. We propose instead to use the squared $L_2$-Wasserstein distance to a standard Gaussian as the ICA contrast. We show that the Wasserstein distance between a standard normal distribution and linear projections of the data is maximized when the projection recovers an i...
| Authors | Ashutosh Jha, Michel Besserve, Simon Buchholz |
|---|---|
| Primary category | cs.LG |
| Published | 15 July 2026 |
| Last updated at source | 7 October 2026 |
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