In this article, we introduce a discriminative ridge regression approach to supervised classification. It estimates a representation model while accounting for discriminativeness between classes, thereby enabling accurate derivation of categorical information. This new type of regression model extends the existing models, such as ridge, lasso, and group lasso, by explicitly incorporating discriminative information. As a special case, we focus on a quadratic model that admits a closed-form analytical solution. The corresponding classifier is called the discriminative ridge machine (DRM). Three iterative algorithms are further established for the DRM to enhance the efficiency and scalability for real applications. Our approach and the algorithms are applicable to general types of data including images, high-dimensional data, and imbalanced data. We compare the DRM with current state-of-the-art classifiers. Our extensive experimental results show the superior performance of the DRM and confirm the effectiveness of the proposed approach.
|Number of pages||15|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|State||Published - Jun 2021|
Bibliographical noteFunding Information:
Manuscript received May 4, 2019; revised December 20, 2019 and April 16, 2020; accepted June 21, 2020. Date of publication July 21, 2020; date of current version June 2, 2021. The work of Chong Peng was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61806106 and in part by the Shandong Provincial Natural Science Foundation, China, under Grant ZR2019QF009. The work of Qiang Cheng was supported in part by NIH under Grant UH3 NS100606-03 and Grant R01HD101508-01 and in part by a grant from the University of Kentucky. (Corresponding author: Qiang Cheng.) Chong Peng is with the College of Computer Science and Technology, Qingdao University, Qingdao 266071, China (e-mail: firstname.lastname@example.org).
© 2012 IEEE.
- high-dimensional data
- imbalanced data
- label information
- ridge regression
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence