Local minima and plateaus in multilayer neural networks
Local minima and plateaus pose a serious problem in learning of neural networks. We investigate the geometric structure of the parameter space of three-layer perceptrons in order to show the existence of local minima and plateaus. It is proved that a critical point of the model with H-1 hidden units always gives a critical point of the model with H hidden units. Based on this result, we prove that the critical point corresponding to the global minimum of a smaller model can be a local minimum or a saddle point of the larger model. We give a necessary and sufficient condition for this. The results are universal in the sense that they do not use special properties of target, loss functions, and activation functions, but only use the hierarchical structure of the model.