Relevance learning for short high-dimensional time series in the life sciences

Digital data characterizing physiological processes over time are becoming increasingly important such as spectrometric data or gene expression profiles. Typical characteristics of such data are high dimensionality due to a fine grained measurement, but usually only few time points of the series. Due to the short length, classical time series models cannot be used. At the same time, due to the high dimensionality, data cannot be treated by means of time windows using simple vectorial techniques. Here, we consider the generative topographic mapping through time (GTM-TT) as a highly regularized model for time series inspection in the unsupervised setting, based on hidden Markov models enhanced with topographic mapping facilities. We extend the model such that supervised classification can be built on top of GTM-TT, resulting in supervised GTM-TT, and we extend the technique by supervised relevance learning. The latter adapts the metric according to given auxiliary information resulting in an interpretable form which can deal with high dimensional inputs. We demonstrate the technique in simulated data as well as an example from the biomedical domain, reaching state of the art classification accuracy in both cases.