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Journal Articles Journal of computational science Year : 2020

An exact line search scheme to accelerate the EM algorithm Application to Gaussian mixture models identification

Abstract

This paper tackles the slowness issue of the well-known expectation-maximization (EM) algorithm in the context of Gaussian mixture models. To cope with this slowness problem, an Exact Line Search scheme is proposed. It is based on exact computation of the step size required to jump, for a given search direction, towards the final solution. Computing this exact step size is easily done by only rooting a second-order polynomial computed from the initial log-likelihood maximization problem. Numerical results using both simulated and real dataset showed the efficiency of the proposed exact line search scheme when applied to the conventional EM algorithm as well as the anti-annealing based acceleration techniques based on either the EM or the expectation conjugate gradient algorithm. © 2020 Elsevier B.V.
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Dates and versions

hal-02534923 , version 1 (09-04-2020)

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Wentao Xiang, Ahmad Karfoul, Chunfeng Yang, Huazhong Shu, Régine Le Bouquin Jeannès. An exact line search scheme to accelerate the EM algorithm Application to Gaussian mixture models identification. Journal of computational science, 2020, 41, pp.101073. ⟨10.1016/j.jocs.2019.101073⟩. ⟨hal-02534923⟩
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