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

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.

► Extension of m-mediods based modeling technique to cater for classes with multimodal PDF.
► A soft classification and anomaly detection adaptive to multimodal distribution of sample.
► Two frameworks are proposed to enable working of proposed classifier for any feature space.
► Proposed MMC-ES is a specialized framework tuned for feature space with computable mean.
► Proposed MMC-GFS framework is applicable to any feature space with computable similarity measure.