Abstract
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.
Original language | English |
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Article number | 9145840 |
Pages (from-to) | 2595-2609 |
Number of pages | 15 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 32 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2021 |
Bibliographical note
Publisher Copyright:© 2012 IEEE.
Keywords
- Discriminative
- high-dimensional data
- imbalanced data
- label information
- ridge regression
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence