Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/5376
Title: | Localized centering: Reducing hubness in large-sample data | Authors: | Hara K. Suzuki I. Shimbo M. Kobayashi K. Fukumizu K. Radovanović, Milan |
Issue Date: | 1-Jun-2015 | Journal: | Proceedings of the National Conference on Artificial Intelligence | Abstract: | Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 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 highdimensional 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 knearest neighbor classification. | URI: | https://open.uns.ac.rs/handle/123456789/5376 | ISBN: | 9781577357025 |
Appears in Collections: | Naučne i umetničke publikacije |
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