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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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