Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33266-1_66 | ICANN (2) |
Keywords | Field | DocType |
automatic relevance weighting scheme,classical time series analysis,time-series data,hidden markov model,generative topographic mapping,data characteristics rule,life science,spectrometric data,time point,relevant time point,high dimensional entry,short time series | Data mining,Time series,Relevance learning,Weighting,Topographic map,Computer science,Learning vector quantization,Curse of dimensionality,Generative topographic mapping,Artificial intelligence,Hidden Markov model,Machine learning | Conference |
Citations | PageRank | References |
4 | 0.71 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank-Michael Schleif | 1 | 427 | 46.59 |
| Bassam Mokbel | 2 | 189 | 14.73 |
| Andrej Gisbrecht | 3 | 195 | 15.60 |
| Leslie Theunissen | 4 | 6 | 1.44 |
| Volker Dürr | 5 | 54 | 10.19 |
| Barbara Hammer | 6 | 2383 | 181.34 |