Sparse on-line Gaussian processes
Sparse on-line Gaussian processes
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DOI：
10.1162/089976602317250933
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发表时间：
2002-03-01
影响因子：
2.9
通讯作者：
Opper, M
中科院分区：
文献类型：
--
作者：
Csató, L;Opper, M
我们开发了一种高斯过程（GP）模型（这是贝叶斯类型的核机）的稀疏表示方法，以克服它们对大型数据集的限制。该方法是基于贝叶斯在线算法的结合，以及对数据的相关子样本的顺序构建，该子样本完全指定了GP模型的预测。通过在再现核希尔伯特空间中使用吸引人的参数化和投影技术，得到了有效参数的递推和后验过程的稀疏高斯近似。这允许预测的传播和贝叶斯误差测量。各种实验证明了我们的方法的重要性和鲁棒性。
We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully specifies the prediction of the GP model. By using an appealing parameterization and projection techniques in a reproducing kernel Hilbert space, recursions for the effective parameters and a sparse gaussian approximation of the posterior process are obtained. This allows for both a propagation of predictions and Bayesian error measures. The significance and robustness of our approach are demonstrated on a variety of experiments.