Adaptive efficient sparse estimator achieving oracle properties
Bibliographic record
Abstract
Compressed Sensing is the new trend in the signal processing context which aims to sample a compressible signal with a rate less than the Nyquist lower bound sampling rate. The main challenge arises due to the non‐convex optimisation problem to be solved in the reconstruction stage. This paper introduces a suitable objective function in order to simultaneously recover the true support of the underlying sparse signal while achieving an acceptable estimation error. Inspired by the well‐known Lasso objective function, we have developed an objective function based on a new penalty denoted by the Linearised Exponentially Decaying (LED) penalty. The comprehensive analysis of the LED based objective function shows that the new approach satisfies the oracle properties, as opposed to the conventional Lasso objective function. Furthermore, we have developed a Sequential Adaptive Coordinate‐wise (SAC) solution for the proposed objective function. The simulation results for the proposed LED‐SAC reconstruction algorithm are given and compared with other state of the art methods. It is shown that LED‐SAC approaches the least mean squared error criterion. Moreover, compared to the other methods, LED‐SAC has much more adaptation rate in terms of tracking the variations in the support of the underlying sparse signal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".