MULTI-STAGE OMP sparse coding using local matching pursuit atoms selection
Bibliographic record
Abstract
A new multi-stage approach based on component extraction is proposed to more efficiently address the sparse representation problem. In each stage a pre-set number of coefficients are chosen for reconstructing each signal component. A global search is performed to extract a lower dimensional sub-dictionary consisting of a sorted set of candidate atoms to represent the signal component, corresponding to the stage. The best representing atoms are then selected from the sub dictionary using the Matching Pursuit (MP) method. Afterwards, the sparse coefficients are updated in the same manner in which the Orthogonal Matching Pursuit (OMP) operates. The proposed method is more efficient that the conventional OMP methods. To evaluate the performance of the proposed method, it is compared to OMP and Stagewise OMP (StOMP), which are conceptually the most similar to the proposed approach. The results illustrate the proposed method is more time efficient than OMP and more robust and sparser than the StOMP.
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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".