[2603.23547] PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA

Statistics > Machine Learning arXiv:2603.23547 (stat) [Submitted on 20 Mar 2026] Title:PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA Authors:Yuan-Hao Wei, Yan-Jie Sun View a PDF of the paper titled PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA, by Yuan-Hao Wei and 1 other authors View PDF HTML (experimental) Abstract:Independent component analysis is a core framework within blind source separation for recovering latent source signals from observed mixtures under statistical independence assumptions. In this work, we propose PDGMM-VAE, a source-oriented variational autoencoder in which each latent dimension, interpreted explicitly as an individual source signal, is assigned its own Gaussian mixture model prior. Unlike conventional VAE formulations with a shared simple prior, the proposed framework imposes per-dimension heterogeneous prior constraints, enabling the model to capture diverse non-Gaussian source statistics and thereby promote source separation under a probabilistic encoder-decoder architecture. Importantly, the parameters of these per-dimension GMM priors are not fixed in advance, but are adaptively learned and automatically refined toward convergence together with the encoder and decoder parameters under the overall training objective. Within this formulation, the encoder serves as a demixing mapping from observations to latent ...