Eigenvector Projection Transformation and Dimension Size Reduction in Remote Sensing Data Processing
Bibliographic record
Abstract
One problem in multidimensional remote sensing data processing is the reduction of information space from multidimensional to three dimensional for RGB color display and visual analysis with minimal loss of important information. Principal component analysis (PCA) has been used, but the resulting three components are not correlated and can be considerably different in terms of information significance. A new projection transformation was tested. In this approach, information structure of the data set is analyzed by using eigen analysis, followed by Household transformation to establish a transformation matrix. Finally the original multidimensional data set is transformed into a lower dimensional feature space in which each feature has the same degree of significance. To enhance the visual display of the resultant data, techniques for scatter adjustment and rotation projection are applied and tested. A test of the method with Landsat TM (Thematic Mapper) data was carried out for geological applications. The results indicate this method is effective and feasible for routine application.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".