Learning relevant time points for time-series data in the life sciences

Authors:
Frank-Michael Schleif;Bassam Mokbel;Andrej Gisbrecht;Leslie Theunissen;Volker Dürr;Barbara Hammer
Affiliations:
CITEC centre of excellence, University of Bielefeld, Bielefeld, Germany;CITEC centre of excellence, University of Bielefeld, Bielefeld, Germany;CITEC centre of excellence, University of Bielefeld, Bielefeld, Germany;CITEC centre of excellence, University of Bielefeld, Bielefeld, Germany;CITEC centre of excellence, University of Bielefeld, Bielefeld, Germany;CITEC centre of excellence, University of Bielefeld, Bielefeld, Germany
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

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Abstract

In the life sciences, short time series with high dimensional entries are becoming more and more popular such as spectrometric data or gene expression profiles taken over time. Data characteristics rule out classical time series analysis due to the few time points, and they prevent a simple vectorial treatment due to the high dimensionality. In this contribution, we successfully use the generative topographic mapping through time (GTM-TT) which is based on hidden Markov models enhanced with a topographic mapping to model such data. We propose an extension of GTM-TT by relevance learning which automatically adapts the model such that the most relevant input variables and time points are emphasized by means of an automatic relevance weighting scheme. We demonstrate the technique in two applications from the life sciences.