Comparative study of feature space projection methods for hyperspectral image classification
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
Feature space projection, or feature projection is an active research topic in machine learning. Some projection methods have been used in remote sensing for dimension reduction, especially for hyperspectral data due to high dimensionality. Projection methods can improve the performance of classifiers susceptible to the Hughes phenomenon. However, the effect of feature projection for more advanced classifiers has not been well-studied, and there are few studies comparing projection methods for hyperspectral image classification. A comprehensive study has been performed on the effect of feature projection for classification using both reduced and full dimensions. The performance of six feature projection methods (PCA, LLE, LDA, LFDA, LMNN, and SPCA) using three classifiers has been explored on three hyperspectral data sets. Results show that the performance of feature projection methods on different classifiers are mainly consistent for different data sets. LFDA achieves the best overall performance considering all data sets and all classifiers.
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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