Multi-task feature learning by using trace norm regularization

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Abstract

Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

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Jiangmei, Z., Binfeng, Y., Haibo, J., & Wang, K. (2017). Multi-task feature learning by using trace norm regularization. Open Physics, 15(1), 674–681. https://doi.org/10.1515/phys-2017-0079

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