论文标题
正交分离的变分傅里叶特征
Orthogonally Decoupled Variational Fourier Features
论文作者
论文摘要
长期以来，稀疏的诱导点一直是将高斯流程适合大数据的标准方法。在过去的几年中，利用协方差内核近似的光谱方法已证明具有竞争力。在这项工作中，我们利用了最近引入的正交分离的变化基础，以结合光谱方法和稀疏诱导点方法。我们表明，该方法与合成和现实数据的最新技术具有竞争力。
Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit approximations of the covariance kernel have shown to be competitive. In this work we exploit a recently introduced orthogonally decoupled variational basis to combine spectral methods and sparse inducing points methods. We show that the method is competitive with the state-of-the-art on synthetic and on real-world data.
