Efficient Data Reduction Via PCA-Guided Quantile Based Sampling
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909986560409600 |
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| author | Hui-Mean, Foo Chang, Yuan-chin Ivan |
| author_facet | Hui-Mean, Foo Chang, Yuan-chin Ivan |
| contents | In large-scale statistical modeling, reducing data size through subsampling is essential for balancing computational efficiency and statistical accuracy. We propose a new method, Principal Component Analysis guided Quantile Sampling (PCA-QS), which projects data onto principal components and applies quantile-based sampling to retain representative and diverse subsets. Compared with uniform random sampling, leverage score sampling, and coreset methods, PCA-QS consistently achieves lower mean squared error and better preservation of key data characteristics, while also being computationally efficient. This approach is adaptable to a variety of data scenarios and shows strong potential for broad applications in statistical computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06375 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Data Reduction Via PCA-Guided Quantile Based Sampling Hui-Mean, Foo Chang, Yuan-chin Ivan Computation Applications 62D05, 65C60, 62Q05 In large-scale statistical modeling, reducing data size through subsampling is essential for balancing computational efficiency and statistical accuracy. We propose a new method, Principal Component Analysis guided Quantile Sampling (PCA-QS), which projects data onto principal components and applies quantile-based sampling to retain representative and diverse subsets. Compared with uniform random sampling, leverage score sampling, and coreset methods, PCA-QS consistently achieves lower mean squared error and better preservation of key data characteristics, while also being computationally efficient. This approach is adaptable to a variety of data scenarios and shows strong potential for broad applications in statistical computing. |
| title | Efficient Data Reduction Via PCA-Guided Quantile Based Sampling |
| topic | Computation Applications 62D05, 65C60, 62Q05 |
| url | https://arxiv.org/abs/2601.06375 |