Radar Target Recognition by Using 2D Locality Sensitive Discriminant Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the images of an aircraft target are much different from each other under various conditions of different observed angle,locality and illumination,many classical dimensional reduction and feature extracting methods are not effective to recognize the aircraft target.A recognition method of radar target is proposed based on two-dimensional locality sensitive discriminant analysis(2DLSDA).Firstly,two graphs respectively representing intra-class and inter-class neighbor relationship are constructed.Then,weight matrixes are calculated out.Finally,two orthogonal transform matrixes are computed out based on Schur decomposition.The projection matrix is obtained and then the dimensionality of the image is reduced.Thus the small-sample-size problem can be overcome.The recognition results on radar targets show that the proposed method is very effective and feasible.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it