Computational neuro-modeling of visual memory: Multimodal imaging and analysis

Mohammed Elanbari, Nawel Nemmour, Othmane Bouhali, Reda Rawi, Ali Sheharyar, Halima Bensmail

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The high dimensionality of functional magnetic resonance imaging (fMRI) data presents major challenges to fMRI pattern classification. Directly applying standard classifiers often results in overfitting or singularity, which limits the generalizability of the results. In this paper, we propose a "Doubly Regularized LOgistic Regression Algorithm" (DR LORA) which penalizes the voxels of the brain that are of no importance for the classification using the Alternating Direction Method of Multipliers (ADMM) and therefore alleviate this overfitting problem. Our algorithm was compared to other classification based algorithms such as Naive Bayes, Random forest and support vector machine. The results show clear performances for our algorithm.

Original languageEnglish
Title of host publicationBrain Informatics and Health - International Conference, BIH 2014, Proceedings
PublisherSpringer Verlag
Pages21-32
Number of pages12
ISBN (Print)9783319098906
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 International Conference on Brain Informatics and Health, BIH 2014 - Warsaw, Poland
Duration: 11 Aug 201414 Aug 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8609 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2014 International Conference on Brain Informatics and Health, BIH 2014
CountryPoland
CityWarsaw
Period11/8/1414/8/14

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Keywords

  • Alternating Direction Method of Multipliers
  • Classification
  • LASSO
  • Logistic Regression
  • fMRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Elanbari, M., Nemmour, N., Bouhali, O., Rawi, R., Sheharyar, A., & Bensmail, H. (2014). Computational neuro-modeling of visual memory: Multimodal imaging and analysis. In Brain Informatics and Health - International Conference, BIH 2014, Proceedings (pp. 21-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8609 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-09891-3_3