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OFAI-TR-2014-01 ( 201kB PDF file)

A Case for Hubness Removal in High-Dimensional Multimedia Retrieval

Dominik Schnitzer, Arthur Flexer, Nenad Tomasev

This work investigates the negative e ffects 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.

Keywords: multimedia, information retrieval, hubness, curse of dimensionality

Citation: Schnitzer D., Flexer A., Tomasev N.: A Case for Hubness Removal in High-Dimensional Multimedia Retrieval, Proceedings of the 36th European Conference on Information Retrieval (ECIR), 2014.