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A Dual Formulation for Probabilistic Principal Component Analysis

arXiv 2307.10078 ↗First posted 19 Jul 2023cs.LG stat.MLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 19 Jul 2023.

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5 of 16 references.

References · 5

  1. 2020Disentangled Representation Learning and Generation with Manifold Optimizationcited by 15
  2. 2016Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learningcited by 7
  3. 2019Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learningcited by 7
  4. 2020Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Dualitycited by 5
  5. 2021Latent Space Exploration Using Generative Kernel PCAcited by 5

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