Generalized Probabilistic Clustering Projection Models for Discrete Data
Bibliographic record
Abstract
Projection and Clustering are two main approaches in text mining. The goal of projection is to map the high-dimensional data into a lower-dimensional latent space, where the clustering task is to categorize data into different groups based on their similarity features. Several methods have been proposed to retrieve relevant information based on the co-occurrence of the data. However, the majority of works do not examine the joint effect of the projection and clustering, especially the effect of the prior distribution in the case of discrete data. For this purpose, in this paper, we propose a novel approach using a probabilistic clustering-projection framework where Dirichlet distribution, generalized Dirichlet distribution, and Beta-Liouville distribution are implemented as priors to study the impacts of prior knowledge in the perplexity of the model. Using a variational EM algorithm we estimate latent variables associated with clustering and projection parameters, iteratively updating the lower bound of the log-likelihood until convergence. Our experimental results demonstrate reduced perplexity using generalized Dirichlet and Beta-Liouville priors compared to Dirichlet. Moreover, we evaluate the model's performance in word projection and document clustering tasks, finding that both generalized Dirichlet and Beta-Liouville outperform Dirichlet in these domains.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".