arXiv:2510. 04758v3 Announce Type: replace Abstract: In this work, we establish the sufficient conditions under which nonlinear Canonical Correlation Analysis (CCA) recovers ground-truth latent factors up to an affine transformation.
By Zhiwei Han, Stefan Matthes, Hao Shen
arXiv:2606. 29261v1 Announce Type: new Abstract: We derive the linear union-of-subspaces (UoS) model for subspace clustering (SC) from the nonlinear mixture model (NMM) used in blind source separation (BSS) to represent a D-dimensional observation vector as an unknown multivariate nonlinear mapping of C latent variables.
By Ivica Kopriva
The paper introduces a method for selecting a small, diverse subset from a large pool by addressing multiple, potentially conflicting notions of diversity. It formulates a fair multi‑view determinant selection problem that maximizes the weakest per‑view log determinant of a size‑k subset, smooths and relaxes the objective to the Stiefel manifold, and derives an adaptive self‑consistent‑field solver with damping and level shifting. The solver operates using only feature‑map products for each view and includes a rounding step via leverage‑score screening followed by fair local refinement.
By Richard Yi Da Xu
arXiv:2609. 36142v1 Announce Type: cross Abstract: In Bayesian inference problems with non-Gaussian observation noise, the posterior is only as accurate as the noise density, and gradient-based samplers need that density and its gradient evaluable pointwise, whether from an explicit expression or from code, and without an inner solve.
By Joshua Chen, Peter Jan van Leeuwen
arXiv:2605. 11607v2 Announce Type: replace-cross Abstract: Probabilistic partial least squares (PPLS) is a central likelihood-based model for two-view learning when one needs both interpretable latent factors and calibrated uncertainty.
By Haoran Hu, Xingce Wang
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.
By Takafumi Kanamori, Yushi Hirose, Shohei Yamamoto