Online Learning of a Dirichlet Process Mixture of Generalized Dirichlet Distributions for Simultaneous Clustering and Localized Feature Selection
Bibliographic record
Abstract
Online algorithms allow data instances to be processed in a sequential way, which is important for large-scale and real-time applications. In this paper, we propose a novel online clustering approach based on a Dirichlet process mixture of generalized Dirichlet (GD) distributions, which can be considered as an extension of the nite GD mixture model to the innite case. Our approach is built on nonparametric Bayesian analysis where the determination of the number of clusters is sidestepped by assuming an innite number of mixture components. Moreover, an unsupervised localized feature selection scheme is integrated with the proposed nonparametric framework to improve the clustering performance. By learning the proposed model in an online manner using a variational approach, all the involved parameters and features saliencies are estimated simultaneously and effectively in closed forms. The proposed online innite mixture model is validated through both synthetic data sets and two challenging real-world applications namely text document clustering and online human face detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".