Building Blocks for Hierarchical Latent Variable Models
Harri Valpola, Tapani Raiko, Juha Karhunen · 2001
We introduce building blocks from which a large variety of latent variable models can be built. The blocks include con-tinuous and discrete variables, summation, addition, non-linearity and switching. Ensemble learning provides a cost function which can be used for updating the variables as well as optimising the model structure. The blocks are de-signed to fit together and to yield efficient update rules. Em-phasis is on local computation which results in linear com-putational complexity. We propose and test a structure with a hierachical nonlinear model for variances and means. 1.