Robust curve clustering based on a multivariate t-distribution model

Authors:
Zhi Min Wang;Qing Song;Yeng Chai Soh;Kang Sim
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;Institute of Mental Health/Woodbridge Hospital, Singapore
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

This brief presents a curve clustering technique based on a new multivariate model. Instead of the usual Gaussian random effect model, our method uses the multivariate t- distribution model which has better robustness to outliers and noise. In our method, we use the B-spline curve to model curve data and apply the mixed-effects model to capture the randomness and covariance of all curves within the same cluster. After fitting the B-spline-based mixed-effects model to the proposed multivariate t-distribution, we derive an expectation-maximization algorithm for estimating the parameters of the model, and apply the proposed approach to the simulated data and the real dataset. The experimental results show that our model yields better clustering results when compared to the conventional Gaussian random effect model.