Computational neuro-modeling of visual memory

Multimodal imaging and analysis

Mohammed El Anbari, 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages21-32
Number of pages12
Volume8609 LNAI
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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Imaging
Imaging techniques
Data storage equipment
Functional Magnetic Resonance Imaging
Overfitting
Modeling
Method of multipliers
Alternating Direction Method
Naive Bayes
Random Forest
Pattern Classification
Voxel
Logistic Regression
Pattern recognition
Dimensionality
Support vector machines
Logistics
Brain
Support Vector Machine
Classifiers

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

El Anbari, M., Nemmour, N., Bouhali, O., Rawi, R., Sheharyar, A., & Bensmail, H. (2014). Computational neuro-modeling of visual memory: Multimodal imaging and analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8609 LNAI, 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

Computational neuro-modeling of visual memory : Multimodal imaging and analysis. / El Anbari, Mohammed; Nemmour, Nawel; Bouhali, Othmane; Rawi, Reda; Sheharyar, Ali; Bensmail, Halima.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8609 LNAI Springer Verlag, 2014. p. 21-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8609 LNAI).

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

El Anbari, M, Nemmour, N, Bouhali, O, Rawi, R, Sheharyar, A & Bensmail, H 2014, Computational neuro-modeling of visual memory: Multimodal imaging and analysis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8609 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8609 LNAI, Springer Verlag, pp. 21-32, 2014 International Conference on Brain Informatics and Health, BIH 2014, Warsaw, Poland, 11/8/14. https://doi.org/10.1007/978-3-319-09891-3_3
El Anbari M, Nemmour N, Bouhali O, Rawi R, Sheharyar A, Bensmail H. Computational neuro-modeling of visual memory: Multimodal imaging and analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8609 LNAI. Springer Verlag. 2014. p. 21-32. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-09891-3_3
El Anbari, Mohammed ; Nemmour, Nawel ; Bouhali, Othmane ; Rawi, Reda ; Sheharyar, Ali ; Bensmail, Halima. / Computational neuro-modeling of visual memory : Multimodal imaging and analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8609 LNAI Springer Verlag, 2014. pp. 21-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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