Smooth and Sparse Regularization for NMF Hyperspectral Unmixing
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, we propose a matrix factorization method for hyperspectral unmixing using the linear mixing model. In this method, we add the arctan functions of the endmembers to the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm of the error in order to exploit the sparse property of the fractional abundances. Most of the energy of spectral signatures of materials is concentrated around the first few subbands resulting in smooth spectral signatures. To exploit this smoothness, we also add a weighted norm of the spectral signatures of the materials and to limit their nonsmooth errors. We propose a multiplicative iterative algorithm to solve this minimization problem as a nonnegative matrix factorization (NMF) problem. We apply our proposed Arctan-NMF method on the synthetic data from real spectral library and compare the performance of Arctan-NMF method with several state-of-the-art unmixing methods. Moreover, we evaluate the efficiency of Arctan-NMF on two different types of real hyperspectral data. Our simulations show that the Arctan-NMF is more effective than the state-of-the-art methods in terms of spectral angle distance and abundance angle distance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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