A Dual Formulation for Probabilistic Principal Component Analysis

2023/07/19 by Henri De Plaen, Johan A. K. Suykens, De Plaen, Henri +1
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2307.10078

openalex publication_date 2023/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

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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