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.