Localized Centering: Reducing Hubness in Large-Sample Data
Localized Centering: Reducing Hubness in Large-Sample Data
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DOI：
10.1609/aaai.v29i1.9629
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
Kazuo Hara;Ikumi Suzuki;M. Shimbo;Kei Kobayashi;K. Fukumizu;Miloš Radovanović
中科院分区：
文献类型：
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作者：
Kazuo Hara;Ikumi Suzuki;M. Shimbo;Kei Kobayashi;K. Fukumizu;Miloš Radovanović
Hubness最近被认为是发生在高维空间中的一种问题现象。在本文中，我们解决了当样本数量很大时发生的不同类型的轮毂。我们研究了高维数据中的中心度与大样本数据中的中心度的区别。其中一个发现是，定心可以减少前者，但对后者不起作用。然后，我们提出了一种新的轮毂减少方法，称为局部定心。它是定心的延伸，但对两种类型的轮毂都有效。使用包含大量文档的真实数据集，我们证明了所提出的方法提高了k-最近邻分类的准确性。
Hubness has been recently identified as a problematic phenomenon occurring in high-dimensional space. In this paper, we address a different type of hubness that occurs when the number of samples is large. We investigate the difference between the hubness in high-dimensional data and the one in large-sample data. One finding is that centering, which is known to reduce the former, does not work for the latter. We then propose a new hub-reduction method, called localized centering. It is an extension of centering, yet works effectively for both types of hubness. Using real-world datasets consisting of a large number of documents, we demonstrate that the proposed method improves the accuracy of k-nearest neighbor classification.