On the characterization, generation, and efficient estimation of the complex multivariate GGD

Mowakeaa, Rami; Boukouvalas, Zois; Cavalcante, Charles C.

doi:10.1109/sam.2016.7569684

Cited by 2 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We conduct a number of experiments that highlight the performance advantage of the proposed methods. In the first set of experiments, we generate synthetic sources over the full range of the shape parameter β ∈ [0.125, 8], using the CMGGD data generation method in (Mowakeaa et al, 2016) that allows for noncircular variables, to demonstrate the full capability of IVA-CMGGD. Then, to better accommodate Algorithm 2 IVA-CMGGD Require: X [1] , .…”

Section: Resultsmentioning

confidence: 99%

“…It is clear that this definition neglects noncircularity by omitting the pseudo-covariance matrix. Instead, we introduce the complex augmented form to incorporate the pseudo-covariance (for details, see (Mowakeaa et al, 2016)):…”

Section: The Cmggd Familymentioning

confidence: 99%

“…In (Mowakeaa et al, 2016), we develop the complex MGGD (CMGGD) family of distributions that generalizes the generalized Gaussian distribution in (Ollila et al, 2012) to noncircular random vectors. We develop an estimator for the CMGGD augmented covariance matrix which incorporates both the classical covariance matrix in addition to the pseudo-covariance matrix within the maximum likelihood framework.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

IVA using complex multivariate GGD: application to fMRI analysis

Mowakeaa

Boukouvalas

Long

2019

Multidim Syst Sign Process

Self Cite

View full text Add to dashboard Cite

Examples of complex-valued random phenomena in science and engineering are abound, and joint blind source separation (JBSS) provides an effective way to analyze multiset data. Thus there is a need for flexible JBSS algorithms for efficient data-driven feature extraction in the complex domain. Independent vector analysis (IVA) is a prominent recent extension of independent component analysis to multivariate sources, i.e., to perform JBSS, but its effectiveness is determined by how well the source models used match the true latent distributions and the optimization algorithm employed. The complex multivariate generalized Gaussian distribution (CMGGD) is a simple, yet effective parameterized family of distributions that account for full second-and higher-order statistics including noncircularity, a property that has been often omitted for convenience. In this paper, we marry IVA and CMGGD to derive, IVA-CMGGD, with a number of numerical optimization implementations including steepest descent, the quasi-Newton method Broyden-Fletcher-Goldfarb-Shanno (BFGS), and its limited-memory sibling limited-memory BFGS (L-BFGS) all in the complex-domain. We demonstrate the performance of our algorithm on simulated data as well as a 14-subject real-world complex-valued functional magnetic resonance imaging (fMRI) dataset against a number of competing algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: The Cmggd Familymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation