DocumentCode
2288790
Title
GTM through time
Author
Bishop, Christopher M. ; Hinton, Geoffrey E. ; Strachan, Iain G D
Author_Institution
Neural Comput. Res. Group, Aston Univ., Birmingham, UK
fYear
1997
fDate
7-9 Jul 1997
Firstpage
111
Lastpage
116
Abstract
The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is trained consists of independent, identically distributed (i.i.d.) vectors. For time series, however, the i.i.d. assumption is a poor approximation. In this paper we show how the GTM algorithm can be extended to model time series by incorporating it as the emission density in a hidden Markov model. Since GTM has discrete hidden states we are able to find a tractable EM algorithm, based on the forward-backward algorithm, to train the model. We illustrate the performance of GTM through time using flight recorder data from a helicopter
Keywords
neural nets; GTM algorithm; GTM through time; discrete hidden states; emission density; flight recorder data; forward-backward algorithm; generative topographic mapping algorithm; helicopter; hidden Markov model; independent identically distributed vectors; neural network training; performance; time series;
fLanguage
English
Publisher
iet
Conference_Titel
Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440)
Conference_Location
Cambridge
ISSN
0537-9989
Print_ISBN
0-85296-690-3
Type
conf
DOI
10.1049/cp:19970711
Filename
607502
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