Semi-Supervised Dictionary Learning Based on Atom Graph Regularization
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Bibliographic record
Abstract
In this paper, we propose a novel unified optimization framework for semi-supervised dictionary learning, which optimizes a graph Laplacian component and the dictionary simultaneously. In the framework, the graph Laplacian is defined on the atoms and the corresponding sparse codings. Since the atoms are more concise and representative than the original training samples, the constructed graph Laplacian can not only effectively capture the manifold structure of training samples, but also be more robust to noise and outliers. Moreover, the dictionary and the graph Laplacian can facilitate each other during the learning iterations. We derive an efficient algorithm by combining the block coordinate descent method with the alternating direction method of multipliers to solve the unified optimization problem. Extensive experimental evaluation on several challenging datasets demonstrates the superior performance of the proposed method.
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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 | 0.000 |
| 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