Clustering Gaussian mixture reduction algorithm based on fuzzy adaptive resonance theory for extended target tracking
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a global Gaussian mixture reduction (GMR) algorithm via clustering, which is based on a fuzzy adaptive resonance theory (FART) neural network architecture. Therefore the authors call the proposed algorithm as GMR based on the fuzzy ART (GMR‐FART) in this study. The architecture of GMR‐FART is similar to that of the FART, however, its choice function, match function and learning update equations are characterised by features of Gaussian mixture (GM). The proposed algorithm automatically forms categories (i.e. the reduced GM components) via a feedback mechanism. The performance of GMR‐FART is evaluated by the normalised integrated squared distance measure which describes the deviation between the original and the reduced GM. The proposed algorithm is tested on both one‐dimensional (1D) and 4D simulation examples, and the results show that the proposed algorithm can accurately approximate the original mixture and requires less computational burden, and is useful in extended target tracking.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it