Document Detail


Robust curve clustering based on a multivariate t-distribution model.
MedLine Citation:
PMID:  20952338     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
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.
Authors:
Zhi Min Wang; Qing Song; Yeng Chai Soh; Kang Sim
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Publication Detail:
Type:  Journal Article     Date:  2010-10-14
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  21     ISSN:  1941-0093     ISO Abbreviation:  IEEE Trans Neural Netw     Publication Date:  2010 Dec 
Date Detail:
Created Date:  2010-12-07     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1976-84     Citation Subset:  IM    
Affiliation:
Nanyang Technological University, Singapore. zwang@i2r.a-star.edu.sg
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