Efficient Data Reduction Via PCA-Guided Quantile Based Sampling

Foo Hui-Mean; Yuan-chin Ivan Chang

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arXiv · 2601.06375

Efficient Data Reduction Via PCA-Guided Quantile Based Sampling

Foo Hui-Mean·Yuan-chin Ivan Chang

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Abstract

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.

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Foo Hui-Mean, Yuan-chin Ivan Chang. 2026-01-10. Efficient Data Reduction Via PCA-Guided Quantile Based Sampling. https://arxiv.org/abs/2601.06375

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