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  A general linear non-Gaussian state-space model: Identifiability, identification, and applications

Zhang, K., & Hyvärinen, A. (2011). A general linear non-Gaussian state-space model: Identifiability, identification, and applications. In C.-N. Hsu, & W. Lee (Eds.), Asian Conference on Machine Learning, 14-15 November 2011, South Garden Hotels and Resorts, Taoyuan, Taiwain (pp. 113-128). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B91C-D Version Permalink: http://hdl.handle.net/21.11116/0000-0001-ACAA-E
Genre: Conference Paper

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 Creators:
Zhang, K1, Author              
Hyvärinen, A, Author
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: State-space modeling provides a powerful tool for system identification and prediction. In linear state-space models the data are usually assumed to be Gaussian and the models have certain structural constraints such that they are identifiable. In this paper we propose a non-Gaussian state-space model which does not have such constraints. We prove that this model is fully identifiable. We then propose an efficient two-step method for parameter estimation: one first extracts the subspace of the latent processes based on the temporal information of the data, and then performs multichannel blind deconvolution, making use of both the temporal information and non-Gaussianity. We conduct a series of simulations to illustrate the performance of the proposed method. Finally, we apply the proposed model and parameter estimation method on real data, including major world stock indices and magnetoencephalography (MEG) recordings. Experimental results are encouraging and show the practical usefulness of the proposed model and method.

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 Dates: 2011-11
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: BibTex Citekey: ZhangH2011
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Title: 3rd Asian Conference on Machine Learning (ACML 2011)
Place of Event: Taoyuan, Taiwan
Start-/End Date: 2011-11-14 - 2011-11-15

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Title: Asian Conference on Machine Learning, 14-15 November 2011, South Garden Hotels and Resorts, Taoyuan, Taiwain
Source Genre: Proceedings
 Creator(s):
Hsu, C-N, Editor
Lee, WS, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 113 - 128 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
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Pages: - Volume / Issue: 20 Sequence Number: - Start / End Page: - Identifier: -