Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning
Bibliographic record
Abstract
The Generalized Adaptive Weighted Recursive Least Squares (GAWRLS) dictionary learning method has shown potential for unsupervised dictionary learning. This paper advances GAWRLS by incorporating classification error as an additional cost to enable supervised learning tasks and introduces the Label Consistency for online supervised dictionary learning in classification tasks. The new method is denoted as Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning (LC-GAWRLS). By incorporating both sparse representation error and classification error into the cost function, LC-GAWRLS enables simultaneous learning of the dictionary and classifier parameters. Particularly, to ensure label consistency, the proposed algorithm introduces a correction weight to adaptively regulate the impact of each training data during the model update, enhancing robustness against variations in training data compared to previous dictionary learning methods. Simulation results on real datasets demonstrate that LC-GAWRLS achieves higher classification accuracy compared to existing state-of-the-art supervised dictionary learning methods, particularly in scenarios with limited training samples per class.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".