A Dual Formulation for Probabilistic Principal Component Analysis

Henri De Plaen, Johan A. K. Suykens
7/19/2023
cs.LGstat.ML

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Duality in Multi-View Restricted Kernel Machines

79% match2023

Rethinking PCA Through Duality

79% match1 repo2025

Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms

78% match1 repo62023

On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis

77% match2023

Probabilistic Geometric Principal Component Analysis with application to neural data

77% match2025

Notes on Kernel Methods in Machine Learning

76% match2025

Cite this paper

@article{plaen2023dual,
  title  = {A Dual Formulation for Probabilistic Principal Component Analysis},
  author = {Henri De Plaen and Johan A. K. Suykens},
  year   = {2023},
  eprint = {2307.10078},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.10078v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2307.10078.svg)](https://codeofpaper.com/paper/2307.10078)

Discussion