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The presentation was presented by Nenad Tomasev at the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2011) in New York, NY, USA on August 30th, 2011.
High-dimensional data are by their very nature often difficult to handle by conventional machine-learning algorithms, which is usually characterized as an aspect of the curse of dimensionality. However, it was shown that some of the arising high-dimensional phenomena can be exploited to increase algorithm accuracy. One such phenomenon is hubness, which refers to the emergence of hubs in high-dimensional spaces, where hubs are influential points included in many k-neighbor sets of other points in the data. This phenomenon was previously used to devise a crisp weighted voting scheme for the k-nearest neighbor classifier. In this paper we go a step further by embracing the soft approach, and propose several fuzzy measures for k-nearest neighbor classification, all based on hubness, which express fuzziness of elements appearing in k-neighborhoods of other points. Experimental evaluation on real data from the UCI repository and the image domain suggests that the fuzzy approach provides a useful measure of confidence in the predicted labels, resulting in improvement over the crisp weighted method, as well the standard kNN classifier.