Ten Times the Fitting Images, Slightly Worse Completion: A Fixed-Rank MNIST Study

Authors

  • Bradley Nelson Author
  • Gabriel Stone Author
  • James Harrison Author

Abstract

Would collecting more complete examples necessarily help a small linear model fill a missing image region? On 10,000 official MNIST test images, we compared rank-32 reconstruction bases fitted to nested samples of 500 and 5,000 training images. The larger fit increased mean squared error inside a common eight-by-eight central mask from 0.138980 to 0.142867. The paired increase was 0.003886, with a conditional image-level 95% interval of 0.002472 to 0.005301. Both fits improved on their respective mean-image baselines. Digit-level averages also moved in different directions. This one-draw result concerns a particular basis, inference penalty, and mask; it does not show that additional data generally harm restoration.

Public handwritten digits with an artificial central mask; a comparison of two linear reconstruction fits, rather than a test of a neural restoration system.

Original publication: October 24, 2026.

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Published

2026-10-24

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Vol. 1 No. 1 (2026): Inaugural Issue

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Copyright (c) 2026 Bradley Nelson, Gabriel Stone, James Harrison

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