Robust finite mixture regression for heterogeneous targets
Robust finite mixture regression for heterogeneous targets
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DOI：
10.1007/s10618-018-0564-z
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发表时间：
2018-04
影响因子：
4.8
通讯作者：
Jian Liang;Kun Chen;Ming Lin;Changshui Zhang;Fei Wang
中科院分区：
文献类型：
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作者：
Jian Liang;Kun Chen;Ming Lin;Changshui Zhang;Fei Wang
有限混合回归（FMR）是指从训练数据集中学习多个回归模型的混合建模方案。他们每个人都负责一个子集。FMR是处理样本异质性的有效方案，其中单个回归模型不足以捕获给定特征的观测样本的条件分布的复杂性。本文提出了一种FMR模型，该模型（1）发现样本聚类，同时对多个不完全混合类型目标进行联合建模;（2）实现任务和聚类组件之间的共享特征选择;（3）检测异常任务或任务间的聚类结构，并容纳离群样本。我们提供了一个高维的学习框架下，我们的模型的非渐近预言机的性能界限。所提出的模型进行评估的合成和现实世界的数据集。结果表明，我们的模型可以达到最先进的性能。
Finite Mixture Regression (FMR) refers to the mixture modeling scheme which learns multiple regression models from the training data set. Each of them is in charge of a subset. FMR is an effective scheme for handling sample heterogeneity, where a single regression model is not enough for capturing the complexities of the conditional distribution of the observed samples given the features. In this paper, we propose an FMR model that (1) finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously, (2) achieves shared feature selection among tasks and cluster components, and (3) detects anomaly tasks or clustered structure among tasks, and accommodates outlier samples. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The proposed model is evaluated on both synthetic and real-world data sets. The results show that our model can achieve state-of-the-art performance.