A new nonlinear dimensionality reduction method with application to hyperspectral image analysis
Bibliographic record
Abstract
In this paper, we propose a new nonlinear dimensionality reduction method by combining Locally Linear Embedding (LLE) with Laplacian Eigenmaps, and apply it to hyperspectral data. LLE projects high dimensional data into a low-dimensional Euclidean space while preserving local topological structures. However, it may not keep the relative distance between data points in the dimension-reduced space as in the original data space. Laplacian Eigenmaps, on the other hand, can preserve the locality characteristics in terms of distances between data points. By combining these two methods, a better locality preserving method is created for nonlinear dimensionality reduction. Experiments conducted in this paper confirms the feasibility of the new method for hyperspectral dimensionality reduction. The new method can find the same number of endmembers as PCA and LLE, but it is more accurate than them in terms of endmember location. Moreover, the new method is better than Laplacian Eigenmap alone because it identifies more pure mineral endmembers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".