Accelerated Multi-Task Online Learning Algorithm for Big Data Stream

Conventional machine learning and data mining techniques with batch computing mode suffer from many limitations when being applied to big data stream analytics tasks. Multi-task online learning framework with stream computing mode is a promising tool for big data stream analysis. However, current multi-task online learning algorithm has low convergence rate, such as O(1/〖KF(〗T〖KF)〗) up to the T-th iteration, and its low convergence rate has become a bottleneck of online algorithm performance. In this paper, we propose a novel multi-task accelerated online learning algorithm, called ADA-MTL(accelerated dual averaging method for multi-task learning), which simultaneously obtains low computational time complexity and optimal convergence rate O(1/T\+2). The proof of a closed-form solution theorem which efficiently updates the weight matrix W\-t at each iteration is provided, and detailed theoretical analysis for the algorithm convergence rate is conducted. The experimental results on real-world datasets demonstrate the merits of the proposed multi-task accelerated online learning algorithm for large-scale dynamic data stream problems. Since this multi-task accelerated online learning algorithm can obviously improve the real-time performance and the scalability for big data stream analysis, it is a realistic method for big data stream analytics tasks.