Learning discontinuities with products-of-sigmoids for switching between local models

Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth functions, we propose a model that learns how to switch discontinuously between local models. The local responsibilities, usually represented by Gaussian kernels, are learned by a product of local sigmoidal classifiers that can represent complex shaped and sharply bounded regions. Local models are incrementally added. A locality prior constrains them to learn only local data---which is the key ingredient for incremental learning with local models.