Nonlinear Orthogonal NMF on the Stiefel Manifold With Graph-Based Total Variation Regularization
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Bibliographic record
Abstract
This paper proposes a novel Nonlinear Orthogonal NMF model with Graph-based Total Variation regularization (GTV) for Multispectral document images decomposition. In this model, a GTV regularization is incorporated to preserve the intrinsic geometrical structure of document content lost by the vectorization of spectral images. A spatial orthogonality constraint over the Stiefel manifold is imposed to improve the sparsity of the solution and ensure its uniqueness. The kernel trick is involved to account for the non-linear correlation inherent to spectral data. We devised an efficient algorithm to solve the formulated problem using the Alternating Direction Method of Multipliers (ADMM). The experimental results on real-world data show that the proposed model achieves better decomposition performance than recent competitive methods and outperforms some traditional state-of-the-art methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it