Analyzing Local Structure in Kernel-Based Learning: Explanation, Complexity, and Reliability Assessment
Analyzing Local Structure in Kernel-Based Learning: Explanation, Complexity, and Reliability Assessment
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DOI：
10.1109/msp.2013.2249294
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发表时间：
2013-06
影响因子：
14.9
通讯作者：
G. Montavon;M. Braun;Tammo Krueger;K. Müller
中科院分区：
文献类型：
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作者：
G. Montavon;M. Braun;Tammo Krueger;K. Müller
在过去的十年中，基于非线性内核的学习方法已在科学和行业中广泛使用，例如分类，回归和排名问题。尽管他们的用户对这项强大的技术的性能感到非常满意，但新兴的需求还需要更好地了解学习机和要解决的数据分析问题。但是，打开非线性黑匣子是一个众所周知的艰难挑战。在这篇综述中，我们报告了一系列最新方法，这些方法可普遍用于使内核方法更透明。特别是，我们讨论了相关的维度估计（RDE），该维度估计值允许评估学习问题的潜在复杂性和噪声结构，从而分别区分高/低复杂性的高/低噪声场景。此外，我们引入了一种基于RDE的新型本地技术，用于量化学习预测的可靠性。最后，我们报告可以解释单个非线性预测的技术。通过这种方式，我们的新方法不仅有助于获得有关非线性信号处理问题本身的进一步知识，而且还扩大了内核方法在实际信号处理应用中的一般实用性。
Over the last decade, nonlinear kernel-based learning methods have been widely used in the sciences and in industry for solving, e.g., classification, regression, and ranking problems. While their users are more than happy with the performance of this powerful technology, there is an emerging need to additionally gain better understanding of both the learning machine and the data analysis problem to be solved. Opening the nonlinear black box, however, is a notoriously difficult challenge. In this review, we report on a set of recent methods that can be universally used to make kernel methods more transparent. In particular, we discuss relevant dimension estimation (RDE) that allows to assess the underlying complexity and noise structure of a learning problem and thus to distinguish high/low noise scenarios of high/low complexity respectively. Moreover, we introduce a novel local technique based on RDE for quantifying the reliability of the learned predictions. Finally, we report on techniques that can explain the individual nonlinear prediction. In this manner, our novel methods not only help to gain further knowledge about the nonlinear signal processing problem itself, but they broaden the general usefulness of kernel methods in practical signal processing applications.