A noiseless code length method (NCLM) to estimate dimensionality of hyperspectral data
Bibliographic record
Abstract
Hyperspectral image analysis has been subjected to many improvements made in past decade. Yet the accurate estimation of dimensionality is still a challenge. Since dimension estimation of the hyperspectral data is the first step in analysis of an image, the accuracy of analysis results highly depends on the accuracy of the dimension estimation step. Mostly, existing methods isolate the process of dimension estimation and process of denoising which leads to an inaccurate estimation of constituent components in the signal. In this paper, the problem of estimating the dimensionality of hyperspectral data using the concept of ldquonoiseless code lengthrdquo is addressed. In our proposed method, NCLM, a set of nested subsets including the hyperspectral data is generated first and then an error comparison approach is utilized by estimating the noiseless data error rather than noisy data error used by the existing methods to find the optimum subset. It has been shown that the estimated noiseless error has a minimum that represents the accurate estimation of the dimensionality of hyperspectral data. The comparison of NCLM to other methods shows a substantial improvement in estimation of dimensionality in hyperspectral imagery.
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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 | 0.006 |
| 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".