Data Clustering Using Online Variational Learning of Finite Scaled Dirichlet Mixture Models
Why this work is in the frame
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Bibliographic record
Abstract
With a massive amount of data created on a daily basis, the ubiquitous demand for data analysis is obvious. Recent development of technology has made machine learning techniques applicable to various problems. In this paper, we emphasize on cluster analysis, an important aspect of data analysis. In other words, being able to automatically discover different groups containing similar data is crucial for further information retrieving and anomaly detection tasks. Thus, we propose an online variational inference framework for finite Scaled Dirichlet mixture models. By efficiently handling large scale data, online approach is capable of enhancing the scalability of finite mixture models for demanding applications in real time. The proposed method can simultaneously update the model's parameters and determine the optimal number of components without the complex computation of conventional Bayesian algorithm. The effectiveness of our model is affirmed with challenging problems including spam detection and image clustering.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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