Comparison of feature extraction methods in dimensionality reduction
Bibliographic record
Abstract
This technical note compares a number of feature extraction methods to determine which method enables higher accuracy of the performed classifications for dimensionality reduction in hyperspectral datasets. Two hyperspectral images were transformed into 10-, 15-, and 20-feature spaces using four unsupervised feature extraction methods (i.e., principal component analysis, maximum noise fraction, locally linear embedding (LLE), and independent component analysis) and one supervised feature extraction method (i.e., nonparametric weighted feature extraction, NWFE). A supervised classifier (i.e., support vector machine) processed a small number of training data and the feature spaces. The classification maps were compared with test samples, and then the classification accuracy of the feature extraction method was evaluated by kappa coefficient. With a 95% confidence interval of hypothesis testing, a 10-feature space could provide sufficient dimension for supervised classification and maximum noise fraction; and LLE outperformed the other feature extraction methods. Because NWFE might be limited by the small number of training samples, its classification performance was lower than those of the other feature extraction methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".