Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines

In this paper, we propose the Convolutional Restricted Boltzmann Machine (CRBM) as a tool for detecting concept drift in time-varying data streams. Recently, it was demonstrated that the Restricted Boltzmann Machine (RBM) can be successfully applied to this task. A properly learned RBM contains information about the data probability distribution. Trained on one part of the stream it can be used to detect possible changes in the incoming data. In this work we replace the fully-connected layer in the standard RBM with the convolutional layer, composing the CRBM. We show that it is more suitable for the drift detection task regarding the image data. Preliminary experimental results demonstrate the usefulness of the CRBM as a tool for drift detection in data streams with such type of data.