Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability
Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability
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发表时间：
2020-06
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通讯作者：
Qin Lu;G. V. Karanikolas;Yanning Shen;G. Giannakis
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作者：
Qin Lu;G. V. Karanikolas;Yanning Shen;G. Giannakis
结合核函数和贝叶斯模型的优点，基于高斯过程（GP）的方法不仅在学习丰富的非线性函数方面有充分的优点，而且还可以量化相关的不确定性。虽然大多数GP方法依赖于单个预选先验，但本工作采用GP先验的加权集合，每个先验都具有属于指定核字典的唯一协方差（核）-这导致了更丰富的学习函数空间。利用谱特征形成的核近似的可扩展性，开发了一个在线交互集成GP框架，共同学习所寻函数，并首次在动态中交互式地选择EGP核。通过遗憾分析，利用最佳固定函数估计量对OI-EGP的性能进行了基准测试。此外，新的OI-EGP适应于动态学习功能。综合数据和实际数据验证了所提方案的有效性。
Combining benefits of kernels with Bayesian models, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also quantifying the associated uncertainty. While most GP approaches rely on a single preselected prior, the present work employs a weighted ensemble of GP priors, each having a unique covariance (kernel) belonging to a prescribed kernel dictionary – which leads to a richer space of learning functions. Leveraging kernel approximants formed by spectral features for scalability, an online interactive ensemble (OI-E) GP framework is developed to jointly learn the sought function, and for the first time select inter-actively the EGP kernel on-the-fly. Performance of OI-EGP is benchmarked by the best fixed function estimator via regret analysis. Furthermore, the novel OI-EGP is adapted to accommodate dynamic learning functions. Synthetic and real data tests demonstrate the e ↵ ectiveness of the proposed schemes.