Patch-Based and Tensor-Patch-Based Dimension Reduction Methods for Hyperspectral Images
Bibliographic record
Abstract
The majority of current dimension reduction methods are restricted to the use of spectral information, when the spatial information is left out. In order to overcome this defect, two different solutions: patch-based and tensor-patch-based approaches, were studied in this paper. This paper applies the two solutions to a group of graph-based dimension reduction methods. We found that the patch-based and tensor-patch-based variations greatly boost the final classification results by 5%-15% from the traditional methods. As graph-based methods heavily rely on the calculation of adjacency graphs/weight matrices, this paper proposed the use of a new method: weighted region covariance matrix, to produce the adjacency graphs/weight matrices. In results, the newly proposed method can further improve the dimension reduction results in both the patch-based and tensor-patch-based methods. To reduce the intense computation in the adjacency graphs/weight matrix calculation, the principle component analysis (PCA) is proposed by this paper as a preprocess step.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".