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

Rui Gao; Filip Tronarp; Zheng Zhao; Simo Särkkä

doi:10.1109/MLSP.2019.8918821

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

Rui Gao, Filip Tronarp, Zheng Zhao, Simo Särkkä

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

3 Sitaatiot (Scopus)

Abstrakti

In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L₁ -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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
Kustantaja	IEEE
Sivumäärä	6
ISBN (elektroninen)	9781728108247
DOI - pysyväislinkit	https://doi.org/10.1109/MLSP.2019.8918821
Tila	Julkaistu - 1 lokak. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Machine Learning for Signal Processing - Pittsburgh, Yhdysvallat Kesto: 13 lokak. 2019 → 16 lokak. 2019 Konferenssinumero: 29

Julkaisusarja

Nimi	IEEE International Workshop on Machine Learning for Signal Processing
Kustantaja	IEEE
ISSN (painettu)	2161-0363
ISSN (elektroninen)	2161-0371

Workshop

Workshop	IEEE International Workshop on Machine Learning for Signal Processing
Lyhennettä	MLSP
Maa/Alue	Yhdysvallat
Kaupunki	Pittsburgh
Ajanjakso	13/10/2019 → 16/10/2019

Pääsy asiakirjaan

10.1109/MLSP.2019.8918821

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Link to publication in Scopus

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Siteeraa tätä

Gao, R., Tronarp, F., Zhao, Z., & Särkkä, S. (2019). Regularized State Estimation and Parameter Learning Via Augmented Lagrangian Kalman Smoother Method. teoksessa Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 Artikkeli 8918821 (IEEE International Workshop on Machine Learning for Signal Processing). IEEE. https://doi.org/10.1109/MLSP.2019.8918821

@inproceedings{07c352697f534d2798b12af0219b8a6f,

title = "Regularized State Estimation and Parameter Learning Via Augmented Lagrangian Kalman Smoother Method",

abstract = "In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L1 -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.",

keywords = "Augmented Lagrangian method, Kalman smoother, Parameter learning, Sparsity, State estimation",

author = "Rui Gao and Filip Tronarp and Zheng Zhao and Simo S{\"a}rkk{\"a}",

year = "2019",

month = oct,

day = "1",

doi = "10.1109/MLSP.2019.8918821",

language = "English",

series = "IEEE International Workshop on Machine Learning for Signal Processing",

publisher = "IEEE",

booktitle = "Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019",

address = "United States",

note = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP ; Conference date: 13-10-2019 Through 16-10-2019",

}

Gao, R, Tronarp, F, Zhao, Z & Särkkä, S 2019, Regularized State Estimation and Parameter Learning Via Augmented Lagrangian Kalman Smoother Method. julkaisussa Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019., 8918821, IEEE International Workshop on Machine Learning for Signal Processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing, Pittsburgh, Pennsylvania, Yhdysvallat, 13/10/2019. https://doi.org/10.1109/MLSP.2019.8918821

Regularized State Estimation and Parameter Learning Via Augmented Lagrangian Kalman Smoother Method. / Gao, Rui; Tronarp, Filip; Zhao, Zheng et al.
Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019. IEEE, 2019. 8918821 (IEEE International Workshop on Machine Learning for Signal Processing).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Regularized State Estimation and Parameter Learning Via Augmented Lagrangian Kalman Smoother Method

AU - Gao, Rui

AU - Tronarp, Filip

AU - Zhao, Zheng

AU - Särkkä, Simo

N1 - Conference code: 29

PY - 2019/10/1

Y1 - 2019/10/1

N2 - In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L1 -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.

AB - In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L1 -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.

KW - Augmented Lagrangian method

KW - Kalman smoother

KW - Parameter learning

KW - Sparsity

KW - State estimation

UR - http://www.scopus.com/inward/record.url?scp=85077706727&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2019.8918821

DO - 10.1109/MLSP.2019.8918821

M3 - Conference article in proceedings

AN - SCOPUS:85077706727

T3 - IEEE International Workshop on Machine Learning for Signal Processing

BT - Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019

PB - IEEE

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 13 October 2019 through 16 October 2019

ER -

Gao R, Tronarp F, Zhao Z, Särkkä S. Regularized State Estimation and Parameter Learning Via Augmented Lagrangian Kalman Smoother Method. julkaisussa Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019. IEEE. 2019. 8918821. (IEEE International Workshop on Machine Learning for Signal Processing). doi: 10.1109/MLSP.2019.8918821

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

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