Latent Variable Models for Dimensionality Reduction
Abstract
Principal coordinate analysis (PCO), as a duality of principal component analysis (PCA), is also a classical method for explanatory data analysis. In this paper we propose a probabilistic PCO by using a normal latent variable model in which maximum likelihood estimation and an expectation-maximization algorithm are respectively devised to calculate the configurations of objects in a low-dimensional Euclidean space. We also devise probabilistic formulations for kernel PCA which is a nonlinear extension of PCA.
Cite
Text
Zhang and Jordan. "Latent Variable Models for Dimensionality Reduction." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.Markdown
[Zhang and Jordan. "Latent Variable Models for Dimensionality Reduction." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.](https://mlanthology.org/aistats/2009/zhang2009aistats-latent/)BibTeX
@inproceedings{zhang2009aistats-latent,
title = {{Latent Variable Models for Dimensionality Reduction}},
author = {Zhang, Zhihua and Jordan, Michael I.},
booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics},
year = {2009},
pages = {655-662},
volume = {5},
url = {https://mlanthology.org/aistats/2009/zhang2009aistats-latent/}
}