PRISM: Pruning for Rank-adaptive Interpretable Segmentation Model with Application to Historical Document Multiband Images
Bibliographic record
Abstract
Multispectral (MS) imaging reveals latent content in historical documents by leveraging material-specific spectral signatures. Low-rank decompositions such as Nonnegative Matrix Factorization (NMF) effectively extract these components, but selecting the appropriate rank remains an open challenge in unsupervised settings. We propose PRISM, a structured autoencoder that embeds a tri-factor NMF into a convolutional architecture and integrates similarity-driven pruning to automatically infer the effective number of components during training. The encoder enforces spatial attention to produce coherent abundance maps, while the decoder reconstructs interpretable spectral signatures through constrained linear decoding. This formulation yields compact, interpretable representations with no supervision or manual rank tuning. A Minimum Description Length criterion estimates the optimal rank directly from the input cube, balancing model complexity with data reconstruction. Experiments on historical manuscripts and cross-domain remote sensing datasets demonstrate PRISM's ability to produce meaningful decompositions, with few components and improved interpretability compared to fixed-rank NMF and deep learning baselines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".