Evaluation and comparison of dimensionality reduction methods and band selection
Bibliographic record
Abstract
For dimensionality reduction (DR) of a hyperspectral data cube or band selection, it is desirable to have one method that is suitable for all remote sensing applications. However, in reality this is not possible. A specific remote sensing application requires a specific DR or band selection method that best suits it. In this paper, the evaluation and comparison of three DR methods‐namely, principal component analysis (PCA), wavelet, and minimum noise fraction (MNF)‐and one band selection method were conducted. Based on the experiments, the following was observed. For endmember extraction, the PCA DR, wavelet DR, and band selection found all five endmembers. However, the MNF DR missed one endmember. For mineral detection, the MNF DR produced a map that is closest to the true map when compared with the other DR methods and band selection method. For classification, the PCA DR produced the highest classification rates whereas the other methods yielded less classification rates.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".