Class-Distributed Learning for Multinomial Logistic Regression with High Dimensional Features and a Large Number of Classes-Reference-Cited by-同舟云学术

Class-Distributed Learning for Multinomial Logistic Regression with High Dimensional Features and a Large Number of Classes

Published:2024-07-22 Issue: Volume: Page:1-12
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Wu Shuyuan¹,Zhou Jing²,Xu Ke³,Wang Hansheng⁴

Affiliation:

1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China

2. Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China

3. School of Statistics, University of International Business and Economics, Beijing, China

4. Guanghua School of Management, Peking University, Beijing, China

Funder

National Natural Science Foundation of China

National Statistical Science Research Project

the Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2362230

Reference52 articles.

1. Aggarwal, C. C., Hinneburg, A., and Keim, D. A. (2001), “On the Surprising Behavior of Distance Metrics in High Dimensional Space,” in International Conference on Database Theory, pp. 420–434, Springer.

2. Distributed testing and estimation under sparse high dimensional models

3. “Distributed Semi-Supervised Learning with Kernel Ridge Regression,”;Chang X.;The Journal of Machine Learning Research,2017

4. Clogg, C. C., and Shihadeh, E. S. (1994), Statistical Models for Ordinal Variables, Thousand Oaks: Sage.

5. Cook, R. D. (1998), Regression Graphics: Ideas for Studying Regressions Through Graphics (Vol. 318), New York: Wiley.