Inversion-Free Sparse Bayesian Learning for Temporally Correlated Signal Recovery
Bibliographic record
Abstract
Compressed sensing recovers the sparse signal from far fewer samples than required by the well-known Nyquist--Shannon sampling theorem to speed up the measure- ment procedure. The sparse signal recovery performance can be significantly improved by exploiting the temporal correlation in the multiple measurement vector model within the framework of sparse Bayesian learning. However, it is inevitable to involve the matrix inversion for existing methods, making the application of these schemes to large datasets impractical. To overcome this bottleneck, in this letter, we propose an inversion-free sparse Bayesian learning algorithm for temporally correlated sparse signal recovery, which is free of any matrix inversion operation and only requires simple addition and multiplication operations. Specifically, utilizing a property for the convex quadratic func- tion, we obtain a lower bound for the likelihood distribution that enables the computationally efficient model inference based on the expectation-maximization algorithm. Simulation results show its superior performance over other state-of-the-art methods in terms of the recovery performance and the running time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".