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arXiv · 2307.10078

A Dual Formulation for Probabilistic Principal Component Analysis

Henri De Plaen·Johan A. K. Suykens
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Abstract

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.

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Henri De Plaen, Johan A. K. Suykens. 2023-07-19. A Dual Formulation for Probabilistic Principal Component Analysis. https://arxiv.org/abs/2307.10078

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