Q-metrics: An efficient formulation of normalized distance functions

A generalized class of normalized distance functions called Q-metrics is described in this paper. The Q-metrics approach relies on a unique functional, using a single bounded parameter lambda, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition...

Full description

Saved in:
Bibliographic Details
Published in2007 IEEE International Conference on Systems, Man and Cybernetics pp. 2108 - 2113
Main Authors Mohamed, M.A., Weimin Xiao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2007
Subjects
Artificial neural networks
Associate members
Computational complexity
Data mining
Euclidian Distance
Extraterrestrial measurements
Feature extraction
Feeds
Mahalanobis Distance
Manhattan Distance
P-Metrics
Pattern recognition
Q-Metrics
Testing
USA Councils
Online AccessGet full text

Cover

More Information
Summary:A generalized class of normalized distance functions called Q-metrics is described in this paper. The Q-metrics approach relies on a unique functional, using a single bounded parameter lambda, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property of the proposed model, a distinguishing and extremely attractive characteristic of the Q-metric function is its low computational complexity. We present a formal mathematical proof that Q-metrics satisfy the standard metric axioms. A novel artificial neural network is completely defined and constructed using Q-metrics. This new network is shown to outperform a conventional feed forward back propagation network with the same size when tested on real data sets.
ISBN:142440990X
9781424409907
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2007.4413597