| Title |
Robust $$\ell _1$$ ℓ 1 Approaches to Computing the Geometric Median and Principal and Independent Components
|
|---|---|
| Published in |
Journal of Mathematical Imaging and Vision, February 2016
|
| DOI | 10.1007/s10851-016-0637-9 |
| Pubmed ID | |
| Authors |
Stephen L. Keeling, Karl Kunisch |
| Abstract |
Robust measures are introduced for methods to determine statistically uncorrelated or also statistically independent components spanning data measured in a way that does not permit direct separation of these underlying components. Because of the nonlinear nature of the proposed methods, iterative methods are presented for the optimization of merit functions, and local convergence of these methods is proved. Illustrative examples are presented to demonstrate the benefits of the robust approaches, including an application to the processing of dynamic medical imaging. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 10% |
| Unknown | 9 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 30% |
| Student > Doctoral Student | 2 | 20% |
| Student > Ph. D. Student | 2 | 20% |
| Student > Master | 2 | 20% |
| Lecturer | 1 | 10% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 2 | 20% |
| Engineering | 2 | 20% |
| Social Sciences | 1 | 10% |
| Computer Science | 1 | 10% |
| Neuroscience | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 2 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 June 2016.
All research outputs
#18,171,970
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Outputs from Journal of Mathematical Imaging and Vision
#213
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#204,797
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Outputs of similar age from Journal of Mathematical Imaging and Vision
#2
of 5 outputs
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