arXiv:2608. 13229v1 Announce Type: cross Abstract: We present the mathematical foundations of linear independent component analysis (ICA) models based on standard literature in a self-contained note.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
arXiv:2607. 14081v1 Announce Type: new Abstract: Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures.
arXiv:2609.21926v1 Announce Type: new Abstract: Many machine learning systems try to explain complex data - like images or financial time series - in terms of hidden, independent factors that generat...
arXiv:2606. 07914v1 Announce Type: cross Abstract: We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weights.
The paper studies the numerical solution of the Beurling‑LASSO (BLASSO) for estimating Gaussian mixture models (GMMs) with unknown numbers of components and unknown diagonal covariance matrices. It introduces a Conic Particle Gradient Descent (CPGD) algorithm that incorporates Riemannian gradient descent to respect the Fisher‑Rao geometry of Gaussian distributions. The authors provide theoretical convergence guarantees, including exponential local convergence under a non‑degeneracy condition related to component separation, and demonstrate through numerical experiments that CPGD is more robust to overspecification of components than the EM algorithm.
arXiv:2505. 20532v2 Announce Type: replace Abstract: This paper studies robust one-shot aggregation for distributed and federated Independent Component Analysis (ICA).