Robust RVM based on spike-slab prior
Robust RVM based on spike-slab prior
复制标题
基于spike-slab先验的鲁棒RVM
DOI：
10.1007/s11767-012-0873-0
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发表时间：
2012-10
期刊：
影响因子：
--
通讯作者：
Jin Wenbo
中科院分区：
文献类型：
--
作者：
Ding Xinghao;Mi Zengyuan;Huang Yue;Jin Wenbo
Although Relevance Vector Machine (RVM) is the most popular algorithms in machine learning and computer vision, outliers in the training data make the estimation unreliable. In the paper, a robust RVM model under non-parametric Bayesian framework is proposed. We decompose the noise term in the RVM model into two components, a Gaussian noise term and a spiky noise term. Therefore the observed data is assumed represented as:y=Dw+s+ewhereDwis the relevance vector component, of whichDis the kernel function matrix andwis the weight matrix,sis the spiky term andeis the Gaussian noise term. A spike-slab sparse prior is imposed on the weight vector,wwhich gives a more intuitive constraint on the sparsity than the Student’s t-distribution described in the traditional RVM. For the spiky component,sa spike-slab sparse prior is also introduced to recognize outliers in the training data effectively. Several experiments demonstrate the better performance over the RVM regression.
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影响因子：
2
作者：
R. Dmochowski
通讯作者：
R. Dmochowski
DOI：
10.1016/j.respe.2020.09.004
发表时间：
2020-10-10
期刊：
Revue D'Epidemiologie et De Sante Publique
影响因子：
--
作者：
La rédaction
通讯作者：
La rédaction
影响因子：
9.3
作者：
通讯作者：
--
影响因子：
5.9
作者：
Craighead FL
通讯作者：
Craighead FL