The weighted EM algorithm and block monitoring
Yasuo Matsuyama · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
The expectation and maximization algorithm (EM algorithm) is generalized so that the learning proceeds according to adjustable weights in terms of probability measures. The method presented, the weighted EM algorithm (or the /spl alpha/-EM algorithm), includes the existing EM algorithm, as a special case. It is further found that this learning structure can work systolically. It is also possible to add monitors to interact with lower systolic subsystems. This is made possible by attaching building blocks of the weighted (or plain) EM learning. Derivation of the whole algorithm is based on generalized divergences. In addition to the discussions on the learning, extensions of basic statistical properties such as Fisher's efficient score, his measure of information and Cramer-Rao's inequality, are given. These appear in update equations of the generalized expectation learning. Experiments show that the presented generalized version contains cases that outperform traditional learning methods.