Randomized Group-Greedy Method for Data-Driven Sensor Selection

Randomized group-greedy methods for sensor selection problems are proposed. The randomized greedy sensor selection algorithm is straightforwardly applied to the group-greedy method, and a customized method is also considered. In the customized method, a part of the shrunken sensor candidates is selected to be the oversampled sensors by the common greedy method, and this strategy compensates for the deterioration of the solution due to shrunken sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and a numerical experiment is conducted using a randomly generated dataset. The proposed method 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 group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the reduced sensor candidates by the randomized algorithm.