Frameworks for multivariate m-mediods based modeling and classification in Euclidean and general feature spaces

Authors:
Shehzad Khalid;Shahid Razzaq
Affiliations:
Department of Computer Science and Engineering, Bahria University, Islamabad 44000, Pakistan;Department of Computing, SEECS, National University of Science and Technology, Islamabad 44000, Pakistan
Venue:
Pattern Recognition
Year:
2012

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Abstract

This paper presents an extension of m-mediods based modeling technique to cater for multimodal distributions of sample within a pattern. The classification of new samples and anomaly detection is performed using a novel classification algorithm which can handle patterns with underlying multivariate probability distributions. We have proposed two frameworks, namely MMC-ES and MMC-GFS, to enable our proposed multivarite m-mediods based modeling and classification approach workable for any feature space with a computable distance metric. MMC-ES framework is specialized for finite dimensional features in Euclidean space whereas MMC-GFS works on any feature space with a computable distance metric. Experimental results using simulated and complex real life dataset show that multivariate m-mediods based frameworks are effective and give superior performance than competitive modeling and classification techniques especially when the patterns exhibit multivariate probability density functions.