Computational Neuro-Modeling of Visual Memory: Multimodal Imaging and Analysis Conference Paper uri icon


  • 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. 2014 Springer International Publishing.

published proceedings

  • Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

author list (cited authors)

  • Elanbari, M., Nemmour, N., Bouhali, O., Rawi, R., Sheharyar, A., & Bensmail, H.

citation count

  • 2

complete list of authors

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

editor list (cited editors)

  • Slezak, D., Tan, A., Peters, J. F., & Schwabe, L.

publication date

  • January 2014