Learning-Based Design of Measurement Matrix with Inter-Column Correlation for Compressive Sensing
Bibliographic record
Abstract
In this paper, a new approach for the design of measurement matrix, Φ, for compressive sensing (CS) in a generic context is proposed. In accordance with well-known classical CS theory, we take the elements of Φ to be random, yet, we include correlations within the elements of the individual columns of Φ. To this end, a new structure for Φ is proposed where the correlations of interest are controlled by a selectable parameter. We aim at optimizing the proposed Φ with respect to the latter correlation parameter by leveraging an appropriate criterion in a learning-based framework. We evaluate the performance of the proposed Φ and compare it with the state-of-the-art literature including random Φ with independent and identically distributed (i.i.d.) elements. Performance advantage of the proposed approach is validated in different CS scenarios.
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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".