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Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method

Gao, Rui ; Tronarp, Filip LU ; Zhao, Zheng and Särkkä, Simo (2019) IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
Abstract
In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L 1 -regularization. Assuming a sparsity prior on the state, the joint state estimation and parameter learning problem is cast as an unconstrained optimization problem. However, when the dimensionality of state or parameters is large, memory requirements and computation of learning algorithms are generally prohibitive. Here, we develop a new augmented Lagrangian Kalman smoother method for solving this problem, where the primal variable update is reformulated as Kalman smoother. The effectiveness of the proposed method for state estimation and parameter learning is demonstrated in spectro-temporal... (More)
In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L 1 -regularization. Assuming a sparsity prior on the state, the joint state estimation and parameter learning problem is cast as an unconstrained optimization problem. However, when the dimensionality of state or parameters is large, memory requirements and computation of learning algorithms are generally prohibitive. Here, we develop a new augmented Lagrangian Kalman smoother method for solving this problem, where the primal variable update is reformulated as Kalman smoother. The effectiveness of the proposed method for state estimation and parameter learning is demonstrated in spectro-temporal estimation tasks using both synthetic and real data. (Less)
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author
; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
conference location
Pittsburgh, United States
conference dates
2019-10-13 - 2019-10-16
external identifiers
  • scopus:85077706727
DOI
10.1109/MLSP.2019.8918821
language
English
LU publication?
no
id
61cd841c-1d1d-42c9-b5b5-964f82628c3e
date added to LUP
2023-08-20 23:00:22
date last changed
2023-11-10 14:09:29
@inproceedings{61cd841c-1d1d-42c9-b5b5-964f82628c3e,
  abstract     = {{In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L 1 -regularization. Assuming a sparsity prior on the state, the joint state estimation and parameter learning problem is cast as an unconstrained optimization problem. However, when the dimensionality of state or parameters is large, memory requirements and computation of learning algorithms are generally prohibitive. Here, we develop a new augmented Lagrangian Kalman smoother method for solving this problem, where the primal variable update is reformulated as Kalman smoother. The effectiveness of the proposed method for state estimation and parameter learning is demonstrated in spectro-temporal estimation tasks using both synthetic and real data.}},
  author       = {{Gao, Rui and Tronarp, Filip and Zhao, Zheng and Särkkä, Simo}},
  booktitle    = {{IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method}},
  url          = {{http://dx.doi.org/10.1109/MLSP.2019.8918821}},
  doi          = {{10.1109/MLSP.2019.8918821}},
  year         = {{2019}},
}