Abstract
This paper addresses the problem of temporal data clustering using a dynamic Gaussian mixture model whose means are considered as latent variables distributed according to random walks. Its final objective is to track the dynamic evolution of some critical railway components using data acquired through embedded sensors. The parameters of the proposed algorithm are estimated by maximum likelihood via the Expectation-Maximization algorithm. In contrast to other approaches as the maximum a posteriori estimation in which the covariance matrices of the random walks have to be fixed by the user, the results of the simulations show the ability of the proposed algorithm to correctly estimate these covariances while keeping a low clustering error rate.
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References
Calabrese, A., Paninski, L.: Kalman filter mixture model for spike sorting of non-stationary data. Journal of Neurosciences Methods 196(1), 159–169 (2011)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1–38 (1977)
DeSarbo, W.S., Cron, W.L.: A maximum likelihood methodology for clusterwise linear regression. Journal of Classification 5, 249–282 (1988)
Durbin, J., Koopman, S.J.: Time series analysis by state space methods. Oxford University Press (2001)
McLachlan, G.J., Peel, D.: Finite Mixture Models. Wiley, New York (2000)
McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley, New York (1997)
Jazwinski, A.H.: Stochastic Processes and Filtering Theory, pp. 201–217. Academic Press, New York (1970)
Shumway, R.H., Stoffer, D.S.: Time series analysis and its applications. Springer (2011)
Ghahramani, Z., Hinton, G.E.: Variational learning for switching state-space models. Neural Computation 12, 963–996 (1998)
Titterington, D.M., Smith, A.F., Makov, U.E.: Statistical Analysis of Finite Mixture Distributions. Wiley, New York (1985)
Wedel, M., DeSarbo, W.S.: A maximum likelihood approach for generalized linear models. Journal of Classification 12, 1–35 (1995)
Chamroukhi, F., Samé, A., Govaert, G., Aknin, P.: Time series modeling by a regression approach based on a latent process. Neural Networks 22(5-6), 593–602 (2009)
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El Assaad, H., Samé, A., Govaert, G., Aknin, P. (2013). Model-Based Clustering of Temporal Data. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_2
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DOI: https://doi.org/10.1007/978-3-642-40728-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
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