Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.

大规模在线内核学习旨在从一系列潜在的无限数据点逐步构建一个高效且可扩展的基于内核的预测模型。当前最先进的大规模在线内核学习专注于提高效率。通过近似获得效率的两种关键方法是 (1) 限制支持向量的数量，以及 (2) 使用近似特征图。他们通常使用具有难以处理维度的特征图的内核。虽然这些方法可以有效地处理大规模数据集，但由于近似值，这种结果是通过影响预测准确性来实现的。我们提供了一种替代方法，将使用的内核置于方法的核心。它专注于创建稀疏且有限维的特征图称为隔离内核的内核。使用这种新方法，实现大规模在线内核学习的上述目标变得非常简单——只需使用隔离内核而不是具有难以处理维度的特征映射的内核。我们表明，使用隔离内核，可以在不牺牲准确性的情况下有效地实现大规模的在线内核学习。