Discriminative Ridge Machine: A Classifier for High-Dimensional Data or Imbalanced Data

Chong Peng, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


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 languageEnglish
Article number9145840
Pages (from-to)2595-2609
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number6
StatePublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2012 IEEE.


  • Discriminative
  • high-dimensional data
  • imbalanced data
  • label information
  • ridge regression

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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