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Erschienen in:
2014 | OriginalPaper | Buchkapitel
A Case for Hubness Removal in High–Dimensional Multimedia Retrieval
verfasst von : Dominik Schnitzer, Arthur Flexer, Nenad Tomašev
Erschienen in: Advances in Information Retrieval
Verlag: Springer International Publishing
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This work investigates the negative effects of hubness on multimedia retrieval systems. Because of a problem of measuring distances in high-dimensional spaces, hub objects are close to an exceptionally large part of the data while anti-hubs are far away from all other data points. In the case of similarity based retrieval, hub objects are retrieved over and over again while anti-hubs are nonexistent in the retrieval lists. We investigate textual, image and music data and show how re-scaling methods can avoid the problem and decisively improve the overall retrieval quality. The observations of this work suggest to make hubness analysis an integral part when building a retrieval system.