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Nearest neighbor voting in high dimensional data: Learning from past occurrences

Tomašev Nenad (Artificial Intelligence Laboratory, Jožef Stefan Institute and Jožef Stefan International Postgraduate School, Ljubljana, Slovenia)
Mladenić Dunja (Artificial Intelligence Laboratory, Jožef Stefan Institute and Jožef Stefan International Postgraduate School, Ljubljana, Slovenia)

Hubness is a recently described aspect of the curse of dimensionality inherent to nearest-neighbor methods. This paper describes a new approach for exploiting the hubness phenomenon in k-nearest neighbor classification. We argue that some of the neighbor occurrences carry more information than others, by the virtue of being less frequent events. This observation is related to the hubness phenomenon and we explore how it affects high-dimensional k-nearest neighbor classification. We propose a new algorithm, Hubness Information k-Nearest Neighbor (HIKNN), which introduces the k-occurrence informativeness into the hubness-aware k-nearest neighbor voting framework. The algorithm successfully overcomes some of the issues with the previous hubness-aware approaches, which is shown by performing an extensive evaluation on several types of high-dimensional data.