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Authors: Xiuling Zhou 1 ; Ping Guo 2 and C. L. Philip Chen 3

Affiliations: 1 Beijing Normal University and Beijing City University, China ; 2 Beijing Normal University, China ; 3 The Faculty of Science & Technology and University of Macau, China

ISBN: 978-989-8425-84-3

Keyword(s): Gaussian classifier, Covariance matrix estimation, Multi-regularization parameters selection, Minimum description length.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: Regularization is a solution to solve the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. And multi-regularization parameters estimation is more difficult than single parameter estimation. In this paper, KLIM_L covariance matrix estimation is derived theoretically based on MDL (minimum description length) principle for the small sample problem with high dimension. KLIM_L is a generalization of KLIM (Kullback-Leibler information measure) which considers the local difference in each dimension. Under the framework of MDL principle, multi-regularization parameters are selected by the criterion of minimization the KL divergence and estimated simply and directly by point estimation which is approximated by two-order Taylor expansion. It costs less computation time to estimate the multi-regularization parameters in KLIM_L than in RDA (regularized discriminant analysis) and in LOOC (leave-one-out covariance matrix estimate) where cross valid ation technique is adopted. And higher classification accuracy is achieved by the proposed KLIM_L estimator in experiment. (More)

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Paper citation in several formats:
Zhou, X.; Guo, P. and Chen, C. (2011). MULTI-REGULARIZATION PARAMETERS ESTIMATION FOR GAUSSIAN MIXTURE CLASSIFIER BASED ON MDL PRINCIPLE.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 112-117. DOI: 10.5220/0003669301120117

@conference{ncta11,
author={Xiuling Zhou. and Ping Guo. and C. L. Philip Chen.},
title={MULTI-REGULARIZATION PARAMETERS ESTIMATION FOR GAUSSIAN MIXTURE CLASSIFIER BASED ON MDL PRINCIPLE},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={112-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003669301120117},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - MULTI-REGULARIZATION PARAMETERS ESTIMATION FOR GAUSSIAN MIXTURE CLASSIFIER BASED ON MDL PRINCIPLE
SN - 978-989-8425-84-3
AU - Zhou, X.
AU - Guo, P.
AU - Chen, C.
PY - 2011
SP - 112
EP - 117
DO - 10.5220/0003669301120117

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