Estimating Dimensionality of Hyperspectral Data Using False Neighbour Method
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".