This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Bello-Orgaz G, Jung J J, Camacho D. Social big data: recent achievements and new challenges. Inf Fusion, 2016, 28: 45–59
Cui Q, Gong Z, Ni W, et al. Stochastic online learning for mobile edge computing: learning from changes. IEEE Commun Mag, 2019, 57: 63–69
Couillet R, Debbah M. Random Matrix Methods for Wireless Communications. Cambridge: Cambridge University Press, 2011
Bai Z, Silverstein J W. Spectral Analysis of Large Dimensional Random Matrices. New York: Springer, 2010
Yang Y, Shen F, Huang Z, et al. Discrete nonnegative spectral clustering. IEEE Trans Knowl Data Eng, 2017, 29: 1834–1845
Michael D, Carroll, D, Jocelyn K, et al. Political polarization in the American public. Pew Research Center, 2014. http://www.people-press.org/2014/06/12/political-polarization-in-the-american-public/
Vallet P, Loubaton P, Mestre X. Improved subspace estimation for multivariate observations of high dimension: the deterministic signals case. IEEE Trans Inform Theor, 2012, 58: 1043–1068
This work was supported in part by National Science Fund for Distinguished Young Scholars (Grant No. 61325006), in part by National Nature Science Foundation of China (Grant No. 61631005), in part by Beijing Municipal Science and Technology Project (Grant No. Z181100003218005), and in part by 111 Project of China (Grant No. B16006).
About this article
Cite this article
Chen, H., Tao, X., Li, N. et al. A data analysis of political polarization using random matrix theory. Sci. China Inf. Sci. 63, 129303 (2020). https://doi.org/10.1007/s11432-019-9841-4