Online Algorithm for Unsupervised Sensor Selection
This work addresses a critical challenge in security and healthcare systems by enabling cost-effective sensor selection without ground truth, though it is incremental as it builds on existing partial monitoring frameworks.
The paper tackles the problem of unsupervised sensor selection, where ground truth labels are unavailable, by learning strategies to balance accuracy and cost under the Weak Dominance property. It develops an online algorithm with sub-linear regret, achieving optimal performance as validated on synthetic and real-world datasets.
In many security and healthcare systems, the detection and diagnosis systems use a sequence of sensors/tests. Each test outputs a prediction of the latent state and carries an inherent cost. However, the correctness of the predictions cannot be evaluated since the ground truth annotations may not be available. Our objective is to learn strategies for selecting a test that gives the best trade-off between accuracy and costs in such Unsupervised Sensor Selection (USS) problems. Clearly, learning is feasible only if ground truth can be inferred (explicitly or implicitly) from the problem structure. It is observed that this happens if the problem satisfies the 'Weak Dominance' (WD) property. We set up the USS problem as a stochastic partial monitoring problem and develop an algorithm with sub-linear regret under the WD property. We argue that our algorithm is optimal and evaluate its performance on problem instances generated from synthetic and real-world datasets.