Multi-task Learning with Application to Water Quality Monitoring
Multi-task learning(MTL)exhibits improved performance in many problems in reality by utilizing the intrinsic features among multiple related tasks.In this paper,the problem of water quality monitoring is considered as multi-task learning,in which different tasks correspond to changes caused by new environment or different spectrometers.An improved learning model is presented describing the relationship between wavelengths and pollutant concentration as well as capturing the task relationships with a low-rank shared structure.Under the assumption that different tasks share some common wavelengths,an optimization problem is proposed with the predictors affected by these features and their corresponding coefficients that vary in different tasks.An alternating minimization algorithm is proposed to solve this problem.Experimental results demonstrate the effectiveness of the proposed algorithm in application.
multi-task learning sparse features water quality monitoring
ZHOU Dalin YU Binfeng JI Haibo
Department of Automation,University of Science and Technology of China,Hefei 230026,China
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
4696-4699
2014-07-28（万方平台首次上网日期，不代表论文的发表时间）