Learning Relevant Time Points for Time-Series Data in the Life Sciences
Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B (2012)
In: ICANN (2). Lecture Notes in Computer Science, 7553. Berlin, Heidelberg: Springer Berlin Heidelberg: 531-539.
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Abstract / Bemerkung
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.
Erscheinungsjahr
2012
Titel des Konferenzbandes
ICANN (2)
Serien- oder Zeitschriftentitel
Lecture Notes in Computer Science
Band
7553
Seite(n)
531-539
ISBN
978-3-642-33265-4
ISSN
0302-9743
Page URI
https://pub.uni-bielefeld.de/record/2534877
Zitieren
Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B. Learning Relevant Time Points for Time-Series Data in the Life Sciences. In: ICANN (2). Lecture Notes in Computer Science. Vol 7553. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 531-539.
Schleif, F. - M., Mokbel, B., Gisbrecht, A., Theunissen, L., Dürr, V., & Hammer, B. (2012). Learning Relevant Time Points for Time-Series Data in the Life Sciences. ICANN (2), Lecture Notes in Computer Science, 7553, 531-539. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-33266-1_66
Schleif, Frank-Michael, Mokbel, Bassam, Gisbrecht, Andrej, Theunissen, Leslie, Dürr, Volker, and Hammer, Barbara. 2012. “Learning Relevant Time Points for Time-Series Data in the Life Sciences”. In ICANN (2), 7553:531-539. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg.
Schleif, F. - M., Mokbel, B., Gisbrecht, A., Theunissen, L., Dürr, V., and Hammer, B. (2012). “Learning Relevant Time Points for Time-Series Data in the Life Sciences” in ICANN (2) Lecture Notes in Computer Science, vol. 7553, (Berlin, Heidelberg: Springer Berlin Heidelberg), 531-539.
Schleif, F.-M., et al., 2012. Learning Relevant Time Points for Time-Series Data in the Life Sciences. In ICANN (2). Lecture Notes in Computer Science. no.7553 Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 531-539.
F.-M. Schleif, et al., “Learning Relevant Time Points for Time-Series Data in the Life Sciences”, ICANN (2), Lecture Notes in Computer Science, vol. 7553, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp.531-539.
Schleif, F.-M., Mokbel, B., Gisbrecht, A., Theunissen, L., Dürr, V., Hammer, B.: Learning Relevant Time Points for Time-Series Data in the Life Sciences. ICANN (2). Lecture Notes in Computer Science. 7553, p. 531-539. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).
Schleif, Frank-Michael, Mokbel, Bassam, Gisbrecht, Andrej, Theunissen, Leslie, Dürr, Volker, and Hammer, Barbara. “Learning Relevant Time Points for Time-Series Data in the Life Sciences”. ICANN (2). Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.Vol. 7553. Lecture Notes in Computer Science. 531-539.
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