Intra- and inter-sparse multiple output regression with application on environmental microbial community study
文献类型：会议论文
| 作者 | Jie Yang; Henry C.M. Leung; S.M. Yiu; Yunpeng Cai; Francis Y.L. Chin |
| 出版日期 | 2013 |
| 会议名称 | 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 |
| 会议地点 | Shanghai, China |
| 英文摘要 | Feature selection is important for many biological studies, especially when the number of available samples is limited (in order of hundreds) while the number of input features is large (in order of millions), such as eQTL (expression quantitative trait loci) mapping, GWAS (genome wide association study) and environmental microbial community study. We study the problem of multiple output regression which leverages the underlying common relationship shared by multiple output features and propose an efficient and accurate approach for feature selection. Our approach considers both intra- and inter-group sparsities. The intergroup sparsity assumes that only small set of input features are related to the output features. The intragroup sparsity assumes that each input features may relate to multiple output features which should have different kinds of sparsity. Most existing methods do not model the intragroup sparsity well by either assuming uniform regularization on each group, i.e. each input feature relates to similar number of output features, or requiring prior knowledge of the relationship of input and output features. By modelling the regression coefficients as a mixture distributions of Laplacian and Gaussian, we can shrink group regression coefficients to be small adaptively and learn the intergroup, intragroup sparsity and shrinkage estimation patterns. Empirical studies on the synthetic and real environmental microbial community datasets show that our model has better predictions on test dataset than existing methods such as Lasso, Elastic Net, dirty model and rMTFL (robust multi-task feature learning). Moreover, by using least angle regression or coordinate descent and projected gradient descent techniques for optimization, we can obtain the optimal regression efficiently. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4988]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Jie Yang,Henry C.M. Leung,S.M. Yiu,et al. Intra- and inter-sparse multiple output regression with application on environmental microbial community study[C]. 见:2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013. Shanghai, China. |
入库方式： OAI收割
来源：深圳先进技术研究院
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