Parameter Estimation For Multivariate Generalized Gaussian Distributions

Frédéric Pascal (1) , Lionel Bombrun (2) , Jean-Yves Tourneret (3, 4) , Yannick Berthoumieu (2)

1 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay (COmUE)
2 IMS - Laboratoire de l'intégration, du matériau au système
3 IRIT-SC - Signal et Communications
4 Toulouse INP - Institut National Polytechnique (Toulouse)

Due to its heavy-tailed and fully parametric form, the multivariate generalized Gaussian distribution (MGGD) has been receiving much attention in signal and image processing applications. Considering the estimation issue of the MGGD parameters, the main contribution of this paper is to prove that the maximum likelihood estimator (MLE) of the scatter matrix exists and is unique up to a scalar factor, for a given shape parameter $\beta\in(0,1)$. Moreover, an estimation algorithm based on a Newton-Raphson recursion is proposed for computing the MLE of MGGD parameters. Various experiments conducted on synthetic and real data are presented to illustrate the theoretical derivations in terms of number of iterations and number of samples for different values of the shape parameter. The main conclusion of this work is that the parameters of MGGDs can be estimated using the maximum likelihood principle with good performance.

Mots clés: fixed point algorithm; covariance matrix estimation; Multivariate generalized Gaussian distribution

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Frédéric Pascal, Lionel Bombrun, Jean-Yves Tourneret, Yannick Berthoumieu. Parameter Estimation For Multivariate Generalized Gaussian Distributions. IEEE Transactions on Signal Processing, 2013, 61 (23), pp.5960-5971. ⟨10.1109/TSP.2013.2282909⟩. ⟨hal-00879851⟩