Multivariate‐bounded Gaussian mixture model with minimum message length criterion for model selection
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
Abstract Bounded support Gaussian mixture model (BGMM) has been proposed for data modelling as an alternative to unbounded support mixture models for the cases when the data lies in bounded support. In this paper, we propose applications of multivariate BGMM in data clustering for more insightful analysis of the model. We also propose minimum message length (MML) criterion for model selection in data clustering using multivariate BGMM. The presented model is applied to data clustering in several speech (TSP and Spoken Digits) and image databases (MNIST and Fashion MNIST). We also propose the application of BGMM in code‐book generation at feature extraction phase. Inspired by the success of bag of visual words approach in computer vision, it is also introduced in speech data representation and validated through experiments presented in this paper. For validation of model selection criterion, MML is applied to different medical, speech and image datasets. Experimental results obtained during the model selection through MML are further compared with seven different model selection criteria. The results presented in the paper demonstrate the effectiveness of BGMM for clustering speech and image databases, code‐book generation through clustering for feature representation and model selection.
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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 | 0.000 | 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 it