Dynamic Latent Classification Model: Towards a More Expressive Model for Dynamic Classification

Monitoring a complex process often involves keeping an eye on hundreds or thousands of sensors to determine whether or not the process is under control. We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics in the process as well as modeling dependences between attributes.