Fast Data-driven Greedy Sensor Selection for Ridge Regression
arxiv(2024)
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
minimization of the cost function of the estimator. Sensor selection which
prevents the 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. Furthermore, we
show that sensor selection can be accelerated by dimensionality reduction of
the target variables without large deterioration of the estimation performance.
The effectiveness of the proposed algorithm is verified for two real-world
datasets. The first dataset is a dataset of sea surface temperature for sensor
selection for reconstructing large data, and the second is a dataset of surface
pressure distribution and yaw angle of a ground vehicle for sensor selection
for estimation. The experiments reveal that the proposed algorithm outperforms
some data-drive selection algorithms including the orthogonal matching pursuit.
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