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Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI
NeuroSpin, I2BM, CEA.
NeuroSpin, I2BM, CEA.
NeuroSpin, I2BM, CEA.ORCID iD: 0000-0001-7119-7646
Centre d'Acquisition et de Traitement des Images (CATI).
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2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l12 penalty) or scattered (l1 penalty) predictive pattern with a very limited relevance. A penalty like Total Variation (TV) that exploits the natural 3D structure of the images can increase the spatial coherence of the weight map. However, TV penalization leads to non-smooth optimization problems that are hard to minimize. We propose an optimization framework that minimizes any combination of l1, l2, and TV penalties while preserving the exact l1 penalty. This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm. We propose an original continuation algorithm that uses successively smaller values of the smoothing parameter to reach a prescribed precision while achieving the best possible convergence rate. This algorithm can be used with other losses or penalties. The algorithm is applied on a classification problem on the ADNI dataset. We observe that the TV penalty does not necessarily improve the prediction but provides a major breakthrough in terms of support recovery of the predictive brain regions.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014.
Keywords [en]
Smoothing methods, Logistic regression, Approximation algorithms, Convergence, Vectors, Prediction algorithms, Neuroimaging
National Category
Other Medical Sciences not elsewhere specified Other Computer and Information Science
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:umu:diva-144027DOI: 10.1109/PRNI.2014.6858517ISBN: 978-1-4799-4149-0 (electronic)OAI: oai:DiVA.org:umu-144027DiVA, id: diva2:1175482
Conference
International Workshop on Pattern Recognition in Neuroimaging, Tübingen, June 4-6, 2014
Available from: 2018-01-18 Created: 2018-01-18 Last updated: 2018-06-09

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Publisher's full texthttp://ieeexplore.ieee.org/document/6858517/

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Löfstedt, Tommy

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf