Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.13582 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913742206271488 |
|---|---|
| author | Luo, Wenya Li, Hua Bai, Zhidong Liu, Zhijun |
| author_facet | Luo, Wenya Li, Hua Bai, Zhidong Liu, Zhijun |
| contents | Quadratic discriminant analysis (QDA) is a widely used method for classification problems, particularly preferable over Linear Discriminant Analysis (LDA) for heterogeneous data. However, QDA loses its effectiveness in high-dimensional settings, where the data dimension and sample size tend to infinity. To address this issue, we propose a novel QDA method utilizing spectral correction and regularization techniques, termed SR-QDA. The regularization parameters in our method are selected by maximizing the Fisher-discriminant ratio. We compare SR-QDA with QDA, regularized quadratic discriminant analysis (R-QDA), and several other competitors. The results indicate that SR-QDA performs exceptionally well, especially in moderate and high-dimensional situations. Empirical experiments across diverse datasets further support this conclusion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13582 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spectrally-Corrected and Regularized QDA Classifier for Spiked Covariance Model Luo, Wenya Li, Hua Bai, Zhidong Liu, Zhijun Machine Learning Statistics Theory Quadratic discriminant analysis (QDA) is a widely used method for classification problems, particularly preferable over Linear Discriminant Analysis (LDA) for heterogeneous data. However, QDA loses its effectiveness in high-dimensional settings, where the data dimension and sample size tend to infinity. To address this issue, we propose a novel QDA method utilizing spectral correction and regularization techniques, termed SR-QDA. The regularization parameters in our method are selected by maximizing the Fisher-discriminant ratio. We compare SR-QDA with QDA, regularized quadratic discriminant analysis (R-QDA), and several other competitors. The results indicate that SR-QDA performs exceptionally well, especially in moderate and high-dimensional situations. Empirical experiments across diverse datasets further support this conclusion. |
| title | Spectrally-Corrected and Regularized QDA Classifier for Spiked Covariance Model |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2503.13582 |