Transductive Bayesian regression via manifold learning of prior data structure
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摘要
During the last decades, many studies have been conducted on performing reliable prediction for high-dimensional data that are usually non-linearly correlated with complex patterns. In this paper, we propose a novel Bayesian regression method via non-linear dimensionality reduction. The method incorporates prior information on the underlying structure of original input features to preserve input–output patterns on reduced features, and to provide distributions of predicted values. To verify the effectiveness of the proposed method, we conducted simulations on benchmark and real-world data. Results showed that the method not only better predicts a distribution of forecast estimates compared with other methods, but also more robust and consistent performance on prediction.
论文关键词:Nonlinear dimension reduction,Manifold learning,Bayesian regression,Transductive learning
论文评审过程:Available online 31 May 2012.
论文官网地址:https://doi.org/10.1016/j.eswa.2012.05.036