Skip Nav Destination
Close Modal
Update search
NARROW
Format
Journal
TocHeadingTitle
Date
Availability
1-1 of 1
Jianmin Jiang
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Publisher: Journals Gateway
Neural Computation (2018) 30 (2): 505–525.
Published: 01 February 2018
FIGURES
| View All (6)
Abstract
View article
PDF
While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint -norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles—enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.