Abstract
The non-uniform Hamming metric (dissimilarity function) is an extension of the Hamming metric where distances depend not only on the number of different features, but also on the concrete pair of values that are different in each feature. In this article a new method for non-uniform Hamming metric learning is developed. The method is based on the assumption that the data are divided into several classes that are formed according to the metric and minimizing the total inner-class squared distance. Numerical experiments confirm that the method recovers approximately the metric from the data. Moreover, using of the recovered metric can improve knn classification of categorical data with the Hamming metric and give competitive performance against other classical classifiers.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.83
Non-uniform Hamming metric learning and classification of categorical data
The non-uniform Hamming metric (dissimilarity function) is an extension of the Hamming metric where distances depend not only on the number of different features, but also on the concrete pair of values that are different in each feature. In this article a new method for non-uniform Hamming metric learning is developed. The method is based on the assumption that the data are divided into several classes that are formed according to the metric and minimizing the total inner-class squared distance. Numerical experiments confirm that the method recovers approximately the metric from the data. Moreover, using of the recovered metric can improve knn classification of categorical data with the Hamming metric and give competitive performance against other classical classifiers.
Recommended Citation
Denisiuk, A.(2026). Non-uniform Hamming metric learning and classification of categorical data. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.83