Helsinki University of Technology, Neural Networks Research Centre
P.O. Box 5400, FIN-02015 HUT, Espoo, Finland
The properties of hierarchical nonlinear factor analysis (HNFA) recently introduced by Valpola and others  are studied by reconstructing missing values. The variational Bayesian learning algorithm for HNFA has linear computational complexity and is able to infer the structure of the model in addition to estimating the parameters. To compare HNFA with other methods, we continued the experiments with speech spectrograms in  comparing nonlinear factor analysis (NFA) with linear factor analysis (FA) and with the self-organising map. Experiments suggest that HNFA lies between FA and NFA in handling nonlinear problems. Furthermore, HNFA gives better reconstructions than FA and it is more reliable than NFA.