102
Views
2
CrossRef citations to date
0
Altmetric
Articles

Krylov subspace solvers for ℓ1 regularized logistic regression method

, , ORCID Icon &
Pages 2738-2751 | Received 12 Sep 2020, Accepted 04 Apr 2021, Published online: 19 Apr 2021

References

  • Chung, J., and S. Gazzola. 2019. Flexible Krylov methods for ℓp regularization. SIAM Journal on Scientific Computing 41 (5):S149–S171.
  • Chung, J., A. K. Saibaba, M. Brown, and E. Westma. 2018. Efficient generalized Golub–Kahan based methods for dynamic inverse problems. Inverse Problems 34 (2):024005. doi: 10.1088/1361-6420/aaa0e1.
  • Cramer, J., and G. Ridder. 1988. The logit model in economics. Statistica Neerlandica 42 (4):297–314. doi: 10.1111/j.1467-9574.1988.tb01241.x.
  • Dua, D., and C. Graff. 2019. UCI Machine learning repository. Irvine, CA: University of California, USA, School of Information and Computer Science. http://archive.ics.uci.edu/ml
  • Efron, B., T. Hastie, I. Johnstone, and R. Tibshirani. 2004. Least angle regression. Annals of Statistics 32 (2):407–99.
  • El Guide, M., K. Jbilou, C. Koukouvinos, and A. Lappa. 2020. Comparative study of L1 regularized logistic regression methods for variable selection. Communications in Statistics – Simulation and Computation. Advance online publication. doi: 10.1080/03610918.2020.1752379.
  • Friedman, J., T. Hastie, and, R. Tibshirani. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33 (1):1–22. doi: 10.18637/jss.v033.i01.
  • Gazzola, S., and J. G. Nagy. 2014. Generalized Arnoldi–Tikhonov method for sparse reconstruction. SIAM Journal on Scientific Computing 36 (2):B225–247. doi: 10.1137/130917673.
  • Golub, G.,. M. Heath, and G. Wahba. 1979. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21 (2):215–23. doi: 10.1080/00401706.1979.10489751.
  • Green, P. J. 1984. Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. Journal of the Royal Statistical Society: Series B (Methodological) 46 (2):149–92. doi: 10.1111/j.2517-6161.1984.tb01288.x.
  • Han, D., L. Ma, and C. Yu. 2008. Financial prediction: Application of logistic regression with factor analysis. 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, 1–4.
  • Hansen, P. C. 1992. Analysis of discrete ill-posed problems by means of the L-curve. SIAM Review 34 (4):561–80. doi: 10.1137/1034115.
  • Hastie, T., R. Tibshirani, and J. H. Friedman. 2003. The elements of statistical learning: Data mining, inference, and prediction. 2nd ed. New York: Springer.
  • Hoffman, J. I. E. 2015. Biostatistics for medical and biomedical practitioners: An interpretative guide for medicine and biology. Amsterdam: Elsevier: Academic Press.
  • King, G., and L. Zeng. 2001. Logistic regression in rare events data. Political Analysis 9 (2):137–63. doi: 10.1093/oxfordjournals.pan.a004868.
  • Komarek, P. 2004. Logistic regression for data mining and high-dimensional classification. Ph.D. thesis; Carnegie Mellon University.
  • Lee, S., H. Lee, P. Abbeel, and A. Ng. 2006. Efficient L1 regularized logistic regression. Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06). Boston, Massachusett, USA.
  • Martin, D. 1977. Early warning of bank failure: A logit regression approach. Journal of Banking & Finance 1 (3):249–76. doi: 10.1016/0378-4266(77)90022-X.
  • Matthews, D. E., and V. T. Farewell. 2015. Using and understanding medical statistics. 5th ed. Switzerland: Karger.
  • McCullagh, P., and J. A. Nelder. 1989. Generalized linear models. London: Chapman and Hall.
  • Myers, R. H., D. C. Montgomery, and G. G. Vining. 2002. Generalized linear models. in with applications in engineering and the sciences. 2nd ed. New York: John Wiley and Sons.
  • Nelder, J. A., and R. W. M. Wedderburn. 1972. Generalized linear models. Journal of Royal Statistical Society, Series A (General) 135 (3):370–84. doi: 10.2307/2344614.
  • Ng, A. Y. 2004. Feature selection, l1 vs. l2 regularization, and rotational invariance. In Proceedings of the twenty-first international conference on Machine learning (ICML ’04). New York: Association for Computing Machinery, p. 78.
  • Plichta, S. B., and E. A. Kelvin. 2013. Munro’s statistical methods for health care research. 6th ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
  • Prasad, K., S. K. Dash, and U. C. Mohanty. 2010. A logistic regression approach for monthly rainfall forecasts in meteorological subdivisions of India based on DEMETER retrospective forecasts. International Journal of Climatology 30 (10):1577–88. doi: 10.1002/joc.2019.
  • Reichel, L., F. Sgallari, and Q. Ye. 2012. Tikhonov regularization based on generalized Krylov subspace methods. Applied Numerical Mathematics 62 (9):1215–28. doi: 10.1016/j.apnum.2010.10.002.
  • Roth, V. 2004. The generalized lasso. IEEE Transactions on Neural Networks 15 (1):16–28. doi: 10.1109/TNN.2003.809398.
  • Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58 (1):267–88. doi: 10.1111/j.2517-6161.1996.tb02080.x.
  • Vanlalhruaia, Y. K., Singh, and N. D. Singh. 2017. Binary face image recognition using logistic regression and neural network. 2017 International conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India. 3883–88.
  • Wang, Z., and X. Sun. 2012. Face recognition based on adaptive kernel logistic regression. In: Advances in future computer and control systems. Advances in intelligent and soft computing, edited by D. Jin and S. Lin, vol. 160. Berlin: Springer.
  • Zaidi, M., and A. A. Ofori-Abebrese. 2016. Forecasting stock market trends by logistic regression and neural networks: evidence from KSA stock market. International Journal of Economics, Commerce and Management 6:220–34.
  • Zhang, Z.,. V. Trevino, S. S. Hoseini, S. Belciug, A. M. Boopathi, P. Zhang, F. Gorunescu, V. Subha, and S. Dai. 2018. Variable selection in logistic regression model with genetic algorithm. Annals of Translational Medicine 6 (3):45. doi: 10.21037/atm.2018.01.15.
  • Zou, H., and, and T. Hastie. 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2):301–20. doi: 10.1111/j.1467-9868.2005.00503.x.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.