Fast Data-Driven Greedy Sensor Selection for Ridge Regression

We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator, which is trained based on the data. The proposed algorithm greedily selects sensors fo...

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Vydáno v:	IEEE sensors journal Ročník 25; číslo 6; s. 10030 - 10045
Hlavní autoři:	Sasaki, Yasuo, Yamada, Keigo, Nagata, Takayuki, Saito, Yuji, Nonomura, Taku
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 15.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Algorithms Computational modeling Cost function Datasets Estimation Greedy algorithms Intelligent sensors Keywords Greedy algorithm Linear programming Linear systems Overfitting Parameters Regularization ridge regression sensor selection Sensors Training Vectors
ISSN:	1530-437X, 1558-1748
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Abstract	We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator, which is trained based on the data. The proposed algorithm greedily selects sensors for minimizing the cost function of the estimator. Sensor selection that prevents overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. The effectiveness of the proposed algorithm is verified for artificial datasets that are generated from linear systems and a real-wold dataset that is aimed for the selection of pressure-sensor locations for estimating the yaw angle of a ground vehicle. The demonstration for the datasets reveals that the proposed algorithm computes a sensor set, resulting in more accurate estimation than existing data-driven selection algorithms in some conditions. Furthermore, it is confirmed that setting a positive regularization parameter in the proposed algorithm leads to accurate estimation when overfitting is problematic.
AbstractList	We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator, which is trained based on the data. The proposed algorithm greedily selects sensors for minimizing the cost function of the estimator. Sensor selection that prevents overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. The effectiveness of the proposed algorithm is verified for artificial datasets that are generated from linear systems and a real-wold dataset that is aimed for the selection of pressure-sensor locations for estimating the yaw angle of a ground vehicle. The demonstration for the datasets reveals that the proposed algorithm computes a sensor set, resulting in more accurate estimation than existing data-driven selection algorithms in some conditions. Furthermore, it is confirmed that setting a positive regularization parameter in the proposed algorithm leads to accurate estimation when overfitting is problematic.
Author	Yamada, Keigo Nonomura, Taku Nagata, Takayuki Saito, Yuji Sasaki, Yasuo
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SubjectTerms	Accuracy Algorithms Computational modeling Cost function Datasets Estimation Greedy algorithms Intelligent sensors Keywords Greedy algorithm Linear programming Linear systems Overfitting Parameters Regularization ridge regression sensor selection Sensors Training Vectors
Title	Fast Data-Driven Greedy Sensor Selection for Ridge Regression
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