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ICMCS
2006
IEEE

A New Study on Distance Metrics as Similarity Measurement

9 years 3 months ago
A New Study on Distance Metrics as Similarity Measurement
Distance metric is widely used in similarity estimation. In this paper we find that the most popular Euclidean and Manhattan distance may not be suitable for all data distributions. A general guideline to establish the relation between a distribution model and its corresponding similarity estimation is proposed. Based on Maximum Likelihood theory, we propose new distance metrics, such as harmonic distance and geometric distance. Because the feature elements may be from heterogeneous sources and usually have different influence on similarity estimation, it is inappropriate to model the distribution as isotropic. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, ro...
Jie Yu, Jaume Amores, Nicu Sebe, Qi Tian
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICMCS
Authors Jie Yu, Jaume Amores, Nicu Sebe, Qi Tian
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