论文标题
具有球形谐波特征的稀疏高斯工艺
Sparse Gaussian Processes with Spherical Harmonic Features
论文作者
论文摘要
我们引入了一类新的域间变异高斯过程（GP），其中数据被映射到单位超孔中，以便使用球形谐波表示。我们的推理方案与变分傅立叶特征相媲美，但并不遭受维数的诅咒，并且导致诱导变量之间的对角线协方差矩阵。这可以加快推理，因为它绕过了将大量协方差矩阵倒置的需求。我们的实验表明，与标准稀疏GPS相比，我们的模型能够适合具有600万个条目的数据集的回归模型，同时保留了最先进的状态。我们还证明了与非轭似的分类方面的竞争性能。
We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations. Our inference scheme is comparable to variational Fourier features, but it does not suffer from the curse of dimensionality, and leads to diagonal covariance matrices between inducing variables. This enables a speed-up in inference, because it bypasses the need to invert large covariance matrices. Our experiments show that our model is able to fit a regression model for a dataset with 6 million entries two orders of magnitude faster compared to standard sparse GPs, while retaining state of the art accuracy. We also demonstrate competitive performance on classification with non-conjugate likelihoods.
