A Dual Formulation for Probabilistic Principal Component Analysis
Machine Learning
2023-07-20 v1 Machine Learning
Abstract
In this paper, we characterize Probabilistic Principal Component Analysis in Hilbert spaces and demonstrate how the optimal solution admits a representation in dual space. This allows us to develop a generative framework for kernel methods. Furthermore, we show how it englobes Kernel Principal Component Analysis and illustrate its working on a toy and a real dataset.
Cite
@article{arxiv.2307.10078,
title = {A Dual Formulation for Probabilistic Principal Component Analysis},
author = {Henri De Plaen and Johan A. K. Suykens},
journal= {arXiv preprint arXiv:2307.10078},
year = {2023}
}
Comments
ICML 2023 Workshop on Duality for Modern Machine Learning (DP4ML). 14 pages (8 main + 5 appendix), 4 figures and 4 tables