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OFAI-TR-2016-05 ( 1901kB PDF file)

Centering versus Scaling for Hubness Reduction

Roman Feldbauer, Arthur Flexer

Hubs and anti-hubs are points that appear very close or very far to many other data points due to a problem of measuring distances in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality affecting many machine learning tasks. We present the first large scale empirical study to compare two competing hubness reduction techniques: scaling and centering. We show that scaling consistently reduces hubness and improves nearest neighbor classification, while centering shows rather mixed results. Support vector classification is mostly unaffected by centering-based hubness reduction.

Keywords: Curse of dimensionality, Hubness, Empirical evaluation, SVM, k-NN classification

Citation: Feldbauer R., Flexer A.: Centering versus Scaling for Hubness Reduction, in Proceedings of the 25th International Conference on Artificial Neural Networks (ICANN'16), Barcelona, Spain, 2016.