# A PCA-based Data Prediction Method

arXiv: https://arxiv.org/abs/2510.09246 · HTML: https://mlsanity.com/paper/2510.09246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 10 Oct 2025.

## Abstract (arXiv)

The problem of choosing appropriate values for missing data is often encountered in the data science. We describe a novel method containing both traditional mathematics and machine learning elements for prediction (imputation) of missing data. This method is based on the notion of distance between shifted linear subspaces representing the existing data and candidate sets. The existing data set is represented by the subspace spanned by its first principal components. Solutions for the case of the Euclidean metric are given.