论文标题
稀疏频谱扭曲了非组织内核学习的输入措施
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
论文作者
论文摘要
我们为学习非平稳核的一般形式的明确，依赖于输入的措施值的经过。尽管固定核无处不在且易于使用，但它们很难适应相对于输入的平滑度变化的功能。拟议的学习算法扭曲输入作为控制标准固定核平滑度的条件高斯措施。这种结构使我们能够捕获数据中的非平稳模式，并提供直观的电感偏见。所得的方法基于稀疏光谱高斯工艺，实现了封闭形式的溶液，并且可以扩展到堆叠结构以捕获更复杂的模式。该方法与合成和现实世界数据集的相关算法进行了广泛的验证。我们证明了翘曲功能的参数数量具有显着的效率。
We establish a general form of explicit, input-dependent, measure-valued warpings for learning nonstationary kernels. While stationary kernels are ubiquitous and simple to use, they struggle to adapt to functions that vary in smoothness with respect to the input. The proposed learning algorithm warps inputs as conditional Gaussian measures that control the smoothness of a standard stationary kernel. This construction allows us to capture non-stationary patterns in the data and provides intuitive inductive bias. The resulting method is based on sparse spectrum Gaussian processes, enabling closed-form solutions, and is extensible to a stacked construction to capture more complex patterns. The method is extensively validated alongside related algorithms on synthetic and real world datasets. We demonstrate a remarkable efficiency in the number of parameters of the warping functions in learning problems with both small and large data regimes.
