Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning

With the enhancement of data collection capabilities, massive streaming data have been accumulated in numerous application scenarios. Specifically, the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors. Existing incremental learning methods are often single-task single-view, which cannot learn shared representations between relevant tasks and views. An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges, utilizing the idea of multi-task multi-view learning. Specifically, the attention mechanism is first used to align different sensor data of different views. In addition, MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning. Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.