Contrast Using a Riemannian Nelder Mead Method
This brief concerns the design and application of a Riemannian Nelder-Mead algorithm to minimize a Hartley-entropy-based contrast function to reliably estimate the sources from their mixtures. Despite its nondifferentiability, the contrast function is endowed with attractive properties such as discriminacy, and hence warrants an effort to be effectively handled by a derivative-free optimizer.
Aside from tailoring the Nelder-Mead technique to the constraint set, namely, oblique manifold, the source separation results attained in an empirical study with quasi-correlated synthetic signals and digital images are presented, which favor the proposed method on a comparative basis.
Related Matlab Projects Titles:
- Bag of Lines (BoL) for Improved Aerial Scene Representation.
- Deformation Corrected Compressed Sensing (DC-CS): A Novel Framework for Accelerated Dynamic MRI.
- Multistrip Bundle Block Adjustment of ZY-3 Satellite Imagery by Rigorous Sensor Model Without Ground Control Point.
- Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging.
- Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing Filter.
- Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection.
- Comb-Push Ultrasound Shear Elastography (CUSE) for Evaluation of Thyroid Nodules: Preliminary In Vivo Results.
- Carotid Intraplaque Neovascularization Quantification Software (CINQS).