论文标题
内核的自适应匹配意味着
Adaptive Matching of Kernel Means
论文作者
论文摘要
作为一个有希望的步骤，如果有某些模式匹配机制，数据分析和特征学习的性能将得到改善。可行的解决方案之一可以指实例的重要性估计，因此，内核平均匹配（KMM）已成为内核机中知识发现和新颖性检测的重要方法。此外，现有的KMM方法集中在具体学习框架上。在这项工作中，提出了一种新颖的核能自适应匹配方法，并采用了高度重要性的选定数据来实现优化的计算效率。此外，可以以建议的方法作为匹配附加数据匹配的广义解决方案进行可扩展学习。与几种最先进的方法相比，对多种现实世界数据集的实验结果表明，所提出的方法能够提供出色的性能，同时可以保留计算效率。
As a promising step, the performance of data analysis and feature learning are able to be improved if certain pattern matching mechanism is available. One of the feasible solutions can refer to the importance estimation of instances, and consequently, kernel mean matching (KMM) has become an important method for knowledge discovery and novelty detection in kernel machines. Furthermore, the existing KMM methods have focused on concrete learning frameworks. In this work, a novel approach to adaptive matching of kernel means is proposed, and selected data with high importance are adopted to achieve calculation efficiency with optimization. In addition, scalable learning can be conducted in proposed method as a generalized solution to matching of appended data. The experimental results on a wide variety of real-world data sets demonstrate the proposed method is able to give outstanding performance compared with several state-of-the-art methods, while calculation efficiency can be preserved.
