Isolation kernel: the X factor in efficient and effective large scale online kernel learning
Isolation kernel: the X factor in efficient and effective large scale online kernel learning
复制标题
DOI：
10.1007/s10618-021-00785-1
复制
发表时间：
2019-07
影响因子：
4.8
通讯作者：
K. Ting;Jonathan R. Wells;T. Washio
中科院分区：
文献类型：
--
作者：
K. Ting;Jonathan R. Wells;T. Washio
大规模在线核学习旨在从一系列潜在的无限数据点中逐步构建一个高效且可扩展的基于核的预测模型。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个关键方法是（1）限制支持向量的数量，以及（2）使用近似特征映射。它们通常采用具有难以处理的维度的特征映射的内核。虽然这些方法可以有效地处理大规模数据集，但由于近似，这种结果是通过牺牲预测精度来实现的。我们提供了一种替代方法，将所使用的内核置于方法的核心。它专注于创建一个称为隔离内核的内核的稀疏和有限维特征映射。使用这种新方法，要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的维度的特征映射的内核。我们表明，使用隔离核，可以有效地实现大规模在线内核学习，而不会牺牲准确性。
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 asparse and finite-dimensional feature mapof 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.