The influence of hubness in nearest-neighbor methods in object recognition
This paper was published in the Proceedings of the 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP 2011), August 25 - 27, 2011 in Cluj-Napoca, Romania.
Object recognition from images is one of the essen- tial problems in automatic image processing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not necessarily related to image data. It has recently come to attention that high dimensional data also exhibit high hubness, which essentially means that some very influential data points appear and these points are referred to as hubs. Unsurprisingly, hubs play a very important role in the nearest neighbor classification. We examine the hubness of various image data sets, under several different feature representations. We also show that it is possible to exploit the observed hubness and improve the recognition accuracy.