Multiplicative Weight for Sparse Generalized Linear Model

Authors

Qianlong Cai, Rice University
Ziyiyang Wang, Wenzhou-Kean University
Shuting Xie, Dalhousie University
Siting Deng, Chang Jun High School International Department

Document Type

Conference Proceeding

Publication Date

1-1-2021

Abstract

Sparse generalized linear model is useful in many fields. In the research, the researchers will learn sparse generalized linear model using different algorithms. The paper determines the better algorithm for learning this model by comparing the convergence rate of mirror descent and projected gradient descent. By implementing the two algorithms and comparing the results, the researchers conclude that the mirror descent converges much faster than the projected gradient descent for learning the sparse generalized linear model. This means the mirror descent algorithm is better for learning this model.

Publication Title

Proceedings - 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2021

First Page Number

245

Last Page Number

248

DOI

10.1109/MLBDBI54094.2021.00053

Recommended Citation

Cai, Qianlong; Wang, Ziyiyang; Xie, Shuting; and Deng, Siting, "Multiplicative Weight for Sparse Generalized Linear Model" (2021). Kean Publications. 1028.
https://digitalcommons.kean.edu/keanpublications/1028