Abstract
Despite the ongoing progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still a challenging task. The difficulties include the very high dimensional feature space and very small sample size, as well as the probably high noise level. In this paper, we apply the stable sparse regression to pick the very few most discriminant features (edges) for the following classification. We considered different noise to signal ratios and sparsity controlling parameters and numerical experiments based on simulated data demonstrate the much better classification performance via the feature selection based on the sparse regression.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Friston, K.J.: Functional and Effective Connectivity: A Review. Brain Connectivity 1(1), 13–36 (2011), doi:10.1089/brain.2011.0008.
Sporns, O.: The Human Connectome: Origins and Challenges (to appear 2013)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 227(2), 210 (2009)
Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences 1(3), 248–272 (2008)
Mahmoudi, A., Takerkart, S., Regragui, F., Boussaoud, D., Brovelli, A.: Multivoxel Pattern Analysis for fMRI Data: A Review. Computational and Mathematical Methods in Medicine, Article ID 961257, 14 (2012)
Li, Y., Namburi, P., Yu, Z., Guan, C., Feng, J., Gu, Z.: Voxel Selection in fMRI Data Analysis Based on Sparse Representation. IEEE Transaction on Biomedical Engineering 56(10) (2009)
Li, Y., Long, J., He, L., Lu, H., Gu, Z., et al.: A Sparse Representation-Based Algorithm for Pattern Localization in Brain Imaging Data Analysis. PLoS ONE 7(12) (2012)
Zhang, J., Cheng, W., Wang, Z., Zhang, Z., Lu, W., Lu, G., Feng, J.: Pattern Classification of Large-Scale Functional Brain Networks: Identification of Informative Neuroimaging Markers for Epileps. PLoS ONES 7(5) (2012)
Carroll, M.K., Cecchi, G.A., Rish, I., Garg, R., Rao, A.R.: Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 44, 112 (2009)
Liu, J., Ji, S., Ye, J.: SLEP: Sparse Learning with Efficient Projections, Arizona State University (2009), http://www.public.asu.edu/~jye02/Software/SLEP
Chang, C., Lin, C.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Su, L., Wang, L., Chen, F., Shen, H., Li, B., et al.: Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI. PLoS ONE 7(5) e36147 (2012), doi:10.1371/journal.pone.0036147
Meinshausen, N., Bühlmann, P.: Stability selection. J. Roy. Statistical Society B 72, 417 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Y., Wu, G., Long, Z., Sheng, J., Zhang, J., Chen, H. (2013). Feature Selection via Sparse Regression for Classification of Functional Brain Networks. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-42057-3_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
eBook Packages: Computer ScienceComputer Science (R0)