Multi-View Oriented GPLVM: Expressiveness and Efficiency
Zi yang, Ying Li, Zhidi Lin, Michael Minyi Zhang, Pablo M. Olmos
Abstract
The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the spectral density and the kernel function. By modeling the spectral density with a bivariate Gaussian mixture, we then derive a generic and expressive kernel termed Next-Gen Spectral Mixture (NG-SM) for MV-GPLVMs. To address the inherent computational inefficiency of the NG-SM kernel, we propose a random Fourier feature approximation. Combined with a tailored reparameterization trick, this approximation enables scalable variational inference for both the model and the unified latent representations. Numerical evaluations across a diverse range of multi-view datasets demonstrate that our proposed method consistently outperforms state-of-the-art models in learning meaningful latent representations.
BibTeX
@inproceedings{
yang2025multiview,
title={Multi-View Oriented {GPLVM}: Expressiveness and Efficiency},
author={Zi yang and Ying Li and Zhidi Lin and Michael Minyi Zhang and Pablo M. Olmos},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=gVtk4lzhcl}
}