Dr. Haiqin Yang, a former Ph.D. student supervised by Prof. Irwin King and Prof. Michael R. Lyu, has been awarded the first prize in the IEEE Hong Kong Section PG Student Paper Contest 2010 for the paper titled "Online Learning for Multi-Task Feature Selection" on May 28, 2011. The other awardees are second and third prizes for postgraduate student contest and three awardees of undergraduate student contest. The IEEE Hong Kong Section organizes the Undergraduate and Postgraduate Student Paper Contests to encourage IEEE student members in Hong Kong to apply their engineering knowledge to research projects in electrical and electronic engineering and to present their work concisely and coherently.


"Learning explanatory features across multiple related tasks, or multi-task feature selection (MTFS), is an important problem in the applications of data mining, machine learning, and bioinformatics. Previous MTFS methods fulfill this task by the batch-mode training. This makes them inefficient when data come in sequence or when the number of training data is so large that they cannot be loaded into the memory simultaneously. In order to tackle these problems, we propose a novel online learning framework to solve the MTFS problem. A main advantage of the online algorithm is its efficiency in both time complexity and memory cost. The weights of the MTFS models at each iteration can be updated by closed-form solutions based on the average of previous subgradients. This yields the worst-case bounds of the time complexity and memory cost both in the order of O(d*Q), where d is the number of feature dimensions and Q is the number of tasks. Moreover, we provide theoretical analysis for the average regret of the online learning algorithms, which also guarantees the convergence rate of the algorithms. Finally, we conduct detailed experiments to show the characteristics and merits of the online learning algorithms in solving the MTFS problem."