# Determinant-Based Fast Greedy Sensor Selection Algorithm

@article{Saito2019DeterminantBasedFG, title={Determinant-Based Fast Greedy Sensor Selection Algorithm}, author={Yuji Saito and Taku Nonomura and Keigo Yamada and Kumi Nakai and Takayuki Nagata and Keisuke Asai and Yasuo Sasaki and Daisuke Tsubakino}, journal={IEEE Access}, year={2019}, volume={9}, pages={68535-68551} }

In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of…

## 32 Citations

### Greedy Sensor Placement for Weighted Linear-Least Squares Estimation under Correlated Noise

- MathematicsArXiv
- 2021

A fast algorithm for greedy sensor selection is presented for a linear reducedordered reconstruction under the assumption of correlated noise on the sensor signals, which accomplishes the maximization of the determinant of the Fisher information matrix in the linear inverse problem.

### Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization

- Computer ScienceIEEE Access
- 2021

The problem of selecting an optimal set of sensors estimating a high-dimensional data is considered and a unified formulation of the objective function based on A-optimality is introduced and proved to be submodular, which provides the lower bound on the performance of the greedy method.

### Data-Driven Sparse Sensor Selection Based on A-Optimal Design of Experiment With ADMM

- Computer ScienceIEEE Sensors Journal
- 2021

The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of multipliers (ADMM) algorithm that is better than the existing greedy and convex relaxation methods in terms ofThe-optimality criterion.

### Efficient Sensor Placement for Signal Reconstruction Based on Recursive Methods

- Computer ScienceIEEE Transactions on Signal Processing
- 2021

A novel sensor placement method using signal reconstruction error as the cost function, sequentially minimize it with greedy procedures, and develops a fast reconstruction–oriented local optimization technique, by deriving update formulae for computationally intensive items.

### Effect of Objective Function on Data-Driven Sparse Sensor Optimization

- Computer Science
- 2020

The results indicate that the greedy method based on D-optimality is the most suitable for high accurate reconstruction with low computational cost.

### Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem

- MathematicsIEEE Signal Processing Letters
- 2021

The randomized subspace Newton convex methods for the sensor selection problem are proposed and the customized method shows superior performance to the straightforward implementation in terms of the quality of sensors and the computational time.

### Data-driven optimal sensor placement for high-dimensional system using annealing machine

- Computer ScienceMechanical Systems and Signal Processing
- 2023

### Nondominated-Solution-Based Multi-Objective Greedy Sensor Selection for Optimal Design of Experiments

- Computer ScienceIEEE Transactions on Signal Processing
- 2022

The results of the test case show that the proposed method not only gives the Pareto-optimal front of the multi-objective optimization problem but also produces sets of sensors in terms of D-, A-, and E-optimality, that are superior to the sets selected by pure greedy methods that consider only a single objective function.

### Randomized Group-Greedy Method for Data-Driven Sensor Selection

- Computer Science
- 2022

Randomized group-greedy methods for sensor selection problems are proposed and can provide better optimization results than those obtained by the original group-Greedy method when a similar computational cost is spent as for the original Groupgreedy method.

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The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of multipliers (ADMM) algorithm that is better than the existing greedy and convex relaxation methods in terms ofThe-optimality criterion.

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