Latent Variable Models for Dimensionality Reduction
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:655-662, 2009.
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.