A Multi-Armed Bandit Approach for Online Monitoring High-Dimensional Streaming Data
We investigate the problem of online monitoring high-dimensional streaming in resources constrained environments, where one has limited capacity in data acquisition, transmission or processing, and needs decide how to smartly observe which local components or features of high-dimensional streaming data at each and every time so as to detect potential anomaly rapidly. In the first part of this talk, we provide an overview of the classical sequential change-point detection problem for real-valued data stream, as well as classical multi-armed bandit algorithms. In the second part of the talk, we present our latest research on efficient scalable schemes for online monitoring high-dimensional data, as well as the corresponding multi-armed bandit versions. Both asymptotic analysis and numerical simulations demonstrate the usefulness of our proposed approach in the context of online monitoring large-scale data streams in resources constrained environments.