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- W3044178239 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." @default.
- W3044178239 created "2020-07-29" @default.
- W3044178239 creator A5054958308 @default.
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- W3044178239 date "2021-06-01" @default.
- W3044178239 modified "2023-10-18" @default.
- W3044178239 title "Discriminative Ridge Machine: A Classifier for High-Dimensional Data or Imbalanced Data" @default.
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- W3044178239 doi "https://doi.org/10.1109/tnnls.2020.3006877" @default.
- W3044178239 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8219475" @default.
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