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基于共享随机效应和特异稀疏效应的混合多任务学习模型

Multi-task learning with shared random effects and specific sparse effects
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摘要 在多任务学习问题中,随机效应(random effects)可能同时存在于所有子任务中,而每个子任务又存在对应的稀疏效应(sparse effects).这在文本分析尤其在对电影评论的情感分析中,尤为常见.在本文中,我们提出一种用于数据中同时存在共享随机效应和特定稀疏效应的混合多任务学习模型,并命名为MSS(multi-task learning with shared random effects and specific sparse effects)模型.在模型的建立过程中,我们利用Bayes框架,针对不同效应的特点设定不同的先验分布和超参数.在模型的求解过程中,我们使用变分推断克服Bayes推断中的计算难题,使MSS模型在大规模数据分析中具备广泛的适应性.通过全面的模拟数据实验和真实数据实验的分析结果,我们展示了MSS模型在模型预测和变量选择方面同时具备随机效应模型(random effects models)和稀疏回归模型(sparse regression models)的优势,相比已有方法大幅提高泛化性能.MSS模型通过对多任务学习模型中不同效应的区分,能够更加有效的识别模型中的共享随机效应和特异稀疏效应,进而增强模型在模型预测和变量选择方面的性能. In multi-task learning scenarios,random effects may be shared among different tasks while each task can have its own sparse effects.This structure has often been observed in the field of sentiment analysis for movie rating.In this study,we consider a multi-task learning problem in the presence of variables with shared random effects and specific sparse effects.To address this issue,we propose MSS(multi-task learning with shared random effects and specific sparse effects).To build this model,appropriate priors for the shared effects and specific effects under the Bayesian framework are considered.To overcome the computational complexity of Bayesian inference,an efficient algorithm is proposed based on variational inference,which is scalable to large-scale data analysis problems.The effectiveness of MSS in prediction and variable selection is demonstrated through comprehensive simulation studies and real data analysis of movie rating.The results demonstrate that the characterization of shared weak effects and task-specific sparse effects can improve the accuracy of prediction and variable selection.
作者 彭毫 王雎 王尧 Hao PENG;Ju WANG;Yao WANG(School of Business Administration,Southwestern University of Finance and Economics,Chengdu 611130,China;School of Management and Economics,University of Electronic Science and Technology of China,Chengdu 611731,China;School of Management,Xi'an Jiaotong University,Xi'an 710049,China)
出处 《中国科学:信息科学》 CSCD 北大核心 2020年第8期1217-1238,共22页 Scientia Sinica(Informationis)
基金 国家自然科学基金(批准号:71472023,11501440)资助项目。
关键词 多任务学习 随机效应 稀疏性 变量选择 BAYES推断 multi-task learning random effects sparsity variable selection Bayesian inference
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