Poster
in
Workshop: Duality Principles for Modern Machine Learning

A Dual Formulation for Probabilistic Principal Component Analysis

Henri De Plaen ⋅ Johan Suykens

Keywords: principal component analysis pca duality kernels kpca Probabilistic Models

Abstract

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.

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