PCA feature extraction for change detection in multidimensional unlabelled streaming data.

While there is a lot of research on change detection based on the streaming classification error, finding changes in multidimensional unlabelled streaming data is still a challenge. Here we propose to apply principal component analysis (PCA) to the training data, and mine the stream of selected principal components for change in the distribution. A recently proposed semi-parametric log-likelihood change detector (SPLL) is applied to the raw and the PCA streams in an experiment involving 26 data sets and an artificially induced change. The results show that feature extraction prior to the change detection is beneficial across different data set types, and specifically for data with multiple balanced classes.