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.
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References
Brady, T.F., Konkle, T., Alvarez, G.A., Oliva, A.: Visual long-term memory has a massive storage capacity for object details. Proc. Natl. Acad. Sci. (2008)
Isola, P., Parikh, D., Torralba, A., Oliva, A.: Understanding the intrinsic memorability of images. In: Advances in Neural Information Processing Systems (NIPS) (2011)
Standing, L.: Learning 10,000 pictures. Quarterly Journal of Experimental Psychology (1973)
Brady, T., Konkle, T., Alvarez, G., Oliva, A.: Are real-world objects represented as bound units? Independent forgetting of different object details from visual memory. Journal of Experimental Psychology: General (2012)
Konkle, T., Brady, T.F., Alvarez, G.A., Oliva, A.: Conceptual distinctiveness supports detailed visual long-term memory for real-world objects. JEP:G (2010)
Isola, P., Xiao, J., Parikh, D., Torralba, A., Oliva, A.: What makes a photograph memorable? IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)
Grosenick, L., Klingenberg, B., Katovich, K., Knutson, B., Taylor, J.: Interpretable whole-brain prediction analysis with GraphNet. NeuroImage 72, 304–321 (2013)
Kim, S., Xing, E.: Statistical estimation of correlated genome associations to a quantitative trait network. PLoS Genetics 5(8), e1000587 (2009)
Tian, Z., Zhang, H., Kuang, R.: Sparse Group Selection on Fused Lasso Components for Identifying Group-Specific DNA Copy Number Variations. In: ICDM 2012, pp. 665–674 (2012)
Zou, H., Hastie, T.: Regularization and Variable Selection via the Elastic Net. J. Royal Stat. Soc. B67, 301–320 (2005)
Donoho, D., Johnstone, I.: Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika 81(3), 425–455 (1994)
Guillaume: SPM. Statistical Parametric Mapping, www.fil.ion.ucl.ac.uk/spm/ (retrieved July 18, 2013)
Isola, P., Xiao, J., Torralba, A., Oliva, A.: What makes an image memorable? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)
Isola, P., Xiao, J., Parikh, D., Torralba, A., Oliva, A.: What makes a photograph memorable? IEEE Pattern Analysis and Machine Intelligence, PAMI (2014)
Bainbridge, W.A., Isola, P., Oliva, A.: The Intrinsic Memorability of Face Images. Journal of Experimental Psychology (in press)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. In: JMLR 12, pp. 2825–2830 (2011)
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Elanbari, M., Nemmour, N., Bouhali, O., Rawi, R., Sheharyar, A., Bensmail, H. (2014). Computational Neuro-Modeling of Visual Memory: Multimodal Imaging and Analysis. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_3
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DOI: https://doi.org/10.1007/978-3-319-09891-3_3
Publisher Name: Springer, Cham
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