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

arXiv: https://arxiv.org/abs/1904.07496 · HTML: https://mlsanity.com/paper/1904.07496/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 4 of 4)

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- [High-Order Conditional Mutual Information Maximization for dealing with High-Order Dependencies in Feature Selection](https://mlsanity.com/paper/2207.08476/) (2022)
- [Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets](https://mlsanity.com/paper/2209.05438/) (2022)

## Abstract (arXiv)

We introduce a discriminative regression approach to supervised classification in this paper. It estimates a representation model while accounting for discriminativeness between classes, thereby enabling accurate derivation of categorical information. This new type of regression models extends existing models such as ridge, lasso, and group lasso through 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 discriminative regression 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 currently state-of-the-art classifiers. Our extensive experimental results show superior performance of the DRM and confirm the effectiveness of the proposed approach.