The Concentration of Fractional Distances
IEEE Transactions on Knowledge and Data Engineering
An empirical analysis of the probabilistic K-nearest neighbour classifier
Pattern Recognition Letters
Multimedia Data Mining: A Systematic Introduction to Concepts and Theory
Multimedia Data Mining: A Systematic Introduction to Concepts and Theory
When is 'nearest neighbour' meaningful: A converse theorem and implications
Journal of Complexity
Bayesian adaptive nearest neighbor
Statistical Analysis and Data Mining
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
The Journal of Machine Learning Research
Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Hubness-Aware shared neighbor distances for high-dimensional k-nearest neighbor classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Co-metric: a metric learning algorithm for data with multiple views
Frontiers of Computer Science: Selected Publications from Chinese Universities
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
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Most machine-learning tasks, including classification, involve dealing with high-dimensional data. It was recently shown that the phenomenon of hubness, inherent to high-dimensional data, can be exploited to improve methods based on nearest neighbors (NNs). Hubness refers to the emergence of points (hubs) that appear among the k NNs of many other points in the data, and constitute influential points for kNN classification. In this paper, we present a new probabilistic approach to kNN classification, naive hubness Bayesian k-nearest neighbor (NHBNN), which employs hubness for computing class likelihood estimates. Experiments show that NHBNN compares favorably to different variants of the kNN classifier, including probabilistic kNN (PNN) which is often used as an underlying probabilistic framework for NN classification, signifying that NHBNN is a promising alternative framework for developing probabilistic NN algorithms.