A Bicriteria-Optimization-Approach-Based Dimensionality-Reduction Model for the Color Display of Hyperspectral Images
Bibliographic record
Abstract
This paper proposes a new nonlinear dimensionality-reduction model based on a bicriteria global optimization approach for the color display of hyperspectral images. The proposed fusion model is derived from two well-known and contradictory criteria of good visualization, which are useful in any multidimensional imagery color display, namely, accuracy, with the preservation of spectral distance criterion, and contrast, guaranteeing that colors are well distinguished or concretely allowing the good separability of each observed existing material in the final visualized color image. An internal parameter allows our algorithm to express the contribution or the importance of these two criteria for a specific application. In this framework, which also can be viewed as a classical Bayesian optimization strategy involving a tradeoff between fidelity to the unreduced (raw) spectral data and the expected highly contrasted resulting mapping, we will show that a hybrid optimization strategy, combining a global and deterministic optimization procedure and a local stochastic search using the Metropolis criterion, can be exploited to efficiently minimize the complex nonlinear objective cost function related to our model. The experiments reported in this paper demonstrate that the proposed model, taking into account these two criteria of good visualization, makes easier and more reliable the interpretation and quick overview of such multidimensional hyperspectral images.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".