Paper ID: 2307.10078

A Dual Formulation for Probabilistic Principal Component Analysis

Henri De Plaen, Johan A. K. Suykens

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.

Submitted: Jul 19, 2023