Simulated annealing & boltzmann Machines: a stochastic approach to combinatorialoptimization & neural computing
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Fitting of mixtures with unspecified number of components using cross validation distance estimate
Computational Statistics & Data Analysis
Learning from Incomplete Data
Ant Colony Optimization
Resolution-Based Complexity Control for Gaussian Mixture Models
Neural Computation
Fast cross-validation of high-breakdown resampling methods for PCA
Computational Statistics & Data Analysis
SMEM Algorithm for Mixture Models
Neural Computation
Bayesian inference on principal component analysis using reversible jump Markov chain Monte Carlo
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Prior hyperparameters in Bayesian PCA
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
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.