Adaptive LASSO hyperspectral unmixing using ADMM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a method of hyperspectral unmixing for the linear regression model is introduced. The proposed algorithm employs an adaptive lasso problem using the alternating direction method of multipliers (ADMM) for unmixing process. Indeed, we formulate a weighted l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm problem under the reasonable given error to reconstruct the fractional abundances and to avoid inconsistent end member selection in a sparse semi-supervised hyperspectral imaging process. We show that this problem can be efficiently solved by appropriate selection of functions and parameters appearing in the ADMM approach. First, we enforce both non-negativity and full additivity constraints of the abundance fractions in the objective function. Then, we apply the ADMM algorithm to solve the acquired optimization problem. Our simulations show that the proposed algorithms outperform the state-of-the-art methods in terms of mean square error and reconstruction signal-to-noise-ratio with reasonably reduced computational costs.
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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 it