文章基本信息

标题：Reducing Hubs with Laplacian-based Kernels
本地全文：下载
作者：Ikumi Suzuki ; Kazuo Hara ; Masashi Shimbo 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2013
卷号：28
期号：3
页码：297-310
DOI：10.1527/tjsai.28.297
出版社：The Japanese Society for Artificial Intelligence
摘要：A ``hub'' is an object closely surrounded by, or very similar to, many other objects in the dataset. Recent studies by Radovanoviç et al. emonstrated that in high dimensional spaces, objects close to the data centroid tend to become hubs. In this paper, we show that the family of kernels based on the graph Laplacian makes all objects in the dataset equally similar to the centroid, and thus they are expected to make less hubs when used as a similarity measure. We investigate this hypothesis using both synthetic and real-world data. It turns out that these kernels suppress hubs in some cases but not always, and the results seem to be affected by the size of the data---a factor not discussed previously. However, for the datasets in which hubs are indeed reduced by the Laplacian-based kernels, these kernels work well in classification and information retrieval tasks. This result suggests that the amount of hubs, which can be readily computed in an unsupervised fashion, can be a yardstick of whether Laplacian-based kernels work effectively for a given data.
关键词：hubness ; graph Laplacian ; kernel