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Overcoming key weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity
Ting Kai Ming*, Federation University; YE ZHU, Monash University; Mark Carman, Monash University; Yue Zhu, Nanjing University
Abstract
This paper introduces the first generic version of data dependent dissimilarity and shows that it provides a better closest match than distance measures for three existing algorithms in clustering, anomaly detection and multi-label classification. For each algorithm, we show that by simply replacing the distance measure with the data dependent dissimilarity measure, it overcomes a key weakness of the otherwise unchanged algorithm.
Filed under: Dimensionality Reduction | Semi-Supervised Learning