Skip to Main content Skip to Navigation
Conference papers

Online estimation of MGGD : the Riemannian Averaged Natural Gradient method

Abstract : Multivariate Generalized Gaussian Distributions (MGGD) are a rich class of multivariate distributions, which have gained importance across many engineering applications (image processing, computer vision, radar and biomedical signal processsing). Unfortunately, estimating the parameters of MGGD leads to non-linear matrix equations, whose solution becomes unpractical in high-dimensional problems, or when dealing with very large datasets. To overcome this difficulty, the present paper proposes a new method for online estimation of MGGD parameters, called the Riemannian Averaged Natural Gradient (RANG) method. The RANG method is suitable for application with high-dimensional and large datasets, since it requires modest memory and computational resources. The present paper formulates this new method, and presents some computer simulations, to showcase its performance. It is seen that, while the RANG method makes less exhaustive use of available data, it still achieves identical performance, to classical maximum-likelihood estimation, for sufficiently large datasets.
Document type :
Conference papers
Complete list of metadata

Cited literature [38 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02530279
Contributor : Yannick Berthoumieu Connect in order to contact the contributor
Submitted on : Thursday, April 2, 2020 - 6:49:23 PM
Last modification on : Wednesday, April 8, 2020 - 1:44:10 AM

File

ZHOU_CAMSAP_2019.pdf
Files produced by the author(s)

Identifiers

Citation

Jialun Zhou, Salem Said, Yannick Berthoumieu. Online estimation of MGGD : the Riemannian Averaged Natural Gradient method. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Dec 2019, Le gosier, France. pp.515-519, ⟨10.1109/CAMSAP45676.2019.9022517⟩. ⟨hal-02530279⟩

Share

Metrics

Record views

32

Files downloads

203