NDLI: Automatic Model Selection by Cross-Validation for Probabilistic PCA

Content Provider	Springer Nature Link
Author	López Rubio, Ezequiel Ortiz de Lazca Lobato, Juan Miguel
Copyright Year	2009
Abstract	The Mixture of Probabilistic Principal Components Analyzers (MPPCA) is a multivariate analysis technique which defines a Gaussian probabilistic model at each unit. The numbers of units and principal directions in each unit are not learned in the original approach. Variational Bayesian approaches have been proposed for this purpose, which rely on assumptions on the probability distributions of the MPPCA parameters. Here we present a different way to solve this problem, where cross-validation and simulated annealing are combined to guide the search for an optimal model selection, providing a structured strategy to escape from suboptimal configurations. This allows to learn the model architecture without the need of any assumptions other than those of the basic PPCA framework. Experimental results are presented, which show the probability density estimation and missing value imputation features of the proposal.
Starting Page	113
Ending Page	132
Page Count	20
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	30
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2009-07-15
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Probabilistic principal components analysis (PPCA) Dimensionality reduction Cross-validation Simulated annealing Missing value imputation Probability density estimation Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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