The Noiseless code-length concept in subspace estimation for low SNR hyperspectral signals
Why this work is in the frame
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Bibliographic record
Abstract
In hyperspectral applications, signal vectors belong to a much lower dimensional subspace than the observed data. The true dimensionality of hyperspectral data is difficult to determine in practice. In the presence of powerful noise, estimation of the number of spectrally distinct signal sources that characterize the hyperspectral data is a challenge. In practice, there is no a priori knowledge of the noise statistics. In this paper, we propose the hyperspectral noiseless code-length (HYNCO) method that exploits the high correlation property of hyperspectral data in adjacent bands. HYNCO uses a multiple regression based method to estimate the second order statistics of the noise signal. Further, a combination of the noiseless code-length concept and the multiple regression method is introduced to estimate the rank of the hyperspectral data. Rank conjecture is obtained by locating the subset that minimizes the reconstruction error defined by the method. The algorithm was applied to both synthetically simulated data and to a real hyperspectral image. Comparing the results with existing methods indicates that this method would strongly improve the accuracy of subspace estimation in extremely noisy applications.
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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