Poster
in
Workshop: Duality Principles for Modern Machine Learning
A Dual Formulation for Probabilistic Principal Component Analysis
Henri De Plaen · Johan Suykens
Keywords: [ Probabilistic Models ] [ kpca ] [ kernels ] [ duality ] [ pca ] [ principal component analysis ]
In this paper, we reformulate the Probabilistic Principal Component Analysis framework 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.