Predictions and Diagnostics in Experimental Data Using Support Vector Regression

In this paper we present a novel support vector machine (SVM) based framework for prognosis and diagnosis. We apply the framework to sparse physics data sets, although the method can easily be extended to other domains. Experiments in applied fields, such as experimental physics, are often complicated and expensive. As a result, experimentalists are unable to conduct as many experiments as they would like, leading to very unbalanced data sets that can be dense in one dimension and very sparse in others. Our method predicts the data values along the sparse dimension providing more information to researchers. Often experiments deviate from expectations due to small misalignments in initial parameters. It can be challenging to distinguish these outlier experiments from those where a real underlying process caused the deviation. Our method detects these outlier experiments. We describe our success at prediction and outlier detection and discuss implications for future applications.