Estimating Dimensionality of Hyperspectral Data Using False Neighbour Method
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Bibliographic record
Abstract
Accurate estimation of dimensionality is a prerequisite step prior to many information extraction methods from hyperspectral images. The estimation is usually conducted through linear transformations. These methods, though manifested in different mathematical forms, are all based on treating hyperspectral images as the data sets produced by linear stochastic processes, which may contradict the physical processes involved in the formation of hyperspectral imagery. We investigate in this study the dimensionality of a hyperspectral data by using a nonlinear time series analysis approach - false neighbour method. The investigation is conducted based on pixels of different land-cover types. It is found that the estimated dimensionality of the hyperspectral data is markedly smaller than that derived based on linear transformations. This indicates that the hyperspectral data can be embedded tightly in a lower dimensional space if nonlinearity is considered. It is also found that dimensionality may change among different land-cover types.
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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