A robust sparse representation algorithm based on adaptive joint dictionary
Bibliographic record
Abstract
Abstract Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face recognition. Aiming at the shortcomings of face feature dictionary not ‘clean’ and noise interference dictionary not ‘representative’ in sparse representation classification model, a new method named as robust sparse representation is proposed based on adaptive joint dictionary (RSR‐AJD). First, a fast low‐rank subspace recovery algorithm based on LogDet function (Fast LRSR‐LogDet) is proposed for accurate low‐rank facial intrinsic dictionary representing the similar structure of human face and low computational complexity. Then, the Iteratively Reweighted Robust Principal Component Analysis (IRRPCA) algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion etc. Finally, the above Fast LRSR‐LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary, which includes the low‐rank facial intrinsic dictionary, the occlusion dictionary and the remaining intra‐class variant dictionary for robust sparse coding. Experiments conducted on four popular databases (AR, Extended Yale B, LFW, and Pubfig) verify the robustness and effectiveness of the authors’ method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".