Bayesian Nonlinear Principal Component Analysis Using Random Fields

Simple item page
dc.creatorLian, Heng
dc.date2008-02-09
dc.date.accessioned2026-07-07T09:19:47Z
dc.date.available2026-07-07T09:19:47Z
dc.descriptionWe propose a novel model for nonlinear dimension reduction motivated by the probabilistic formulation of principal component analysis. Nonlinearity is achieved by specifying different transformation matrices at different locations of the latent space and smoothing the transformation using a Markov random field type prior. The computation is made feasible by the recent advances in sampling from von Mises-Fisher distributions.
dc.identifierhttps://arxiv.org/abs/0802.1258
dc.identifierhttp://arxiv.org/abs/0802.1258
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/154517
dc.subjectComputer Vision and Pattern Recognition
dc.subjectMachine Learning
dc.titleBayesian Nonlinear Principal Component Analysis Using Random Fields
dc.typetext

Files

Collections

arXiv