论文标题
基于相似性的贝叶斯混合物模型
A similarity-based Bayesian mixture-of-experts model
论文作者
论文摘要
我们提出了一种新的非参数混合物模型，用于多变量回归问题，灵感来自概率K-Neartheard邻居算法。使用有条件指定的模型，样本外输入的预测基于与每个观察到的数据点的相似性，产生了高斯混合物表示的预测分布。在混合物组件的参数以及距离度量标准的参数上，使用平均场变化贝叶斯算法进行后推断，并具有基于随机梯度的优化过程。在与数据大小相比，输入 - 输出关系相比相比，输入相对较高的设置，该方法尤其有利，而输入输出关系很复杂，并且可以偏向预测分布或多模式。在五个数据集上进行的计算研究，其中两个是合成生成的，这说明了我们的高维输入方法的混合物方法的明显优势，在验证指标和视觉检查方面都优于竞争者模型。
We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k-nearest neighbors algorithm. Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point, yielding predictive distributions represented by Gaussian mixtures. Posterior inference is performed on the parameters of the mixture components as well as the distance metric using a mean-field variational Bayes algorithm accompanied with a stochastic gradient-based optimization procedure. The proposed method is especially advantageous in settings where inputs are of relatively high dimension in comparison to the data size, where input-output relationships are complex, and where predictive distributions may be skewed or multimodal. Computational studies on five datasets, of which two are synthetically generated, illustrate clear advantages of our mixture-of-experts method for high-dimensional inputs, outperforming competitor models both in terms of validation metrics and visual inspection.
