Low-Rank Matrix Factorization Induced Adaptive Divergent Graph Learning for Fuzzy Clustering
Bibliographic record
Abstract
This paper proposes Low-rank matrix factorization induced Adaptive divergent Graph learning for Fuzzy Clustering (LAGFC). This is a clustering model that unifies low-rank matrix factorization, adaptive graph learning, and fuzzy clustering to holistically exploit both global and local structural information. Unlike conventional graph-based methods that decouple graph construction and label inference into separate steps, LAGFC directly generates cluster labels by incorporating a divergence regularization term during adaptive graph learning, enabling end-to-end optimization. To enhance robustness against noisy data, the proposed method adopts the Maximum Correntropy Criterion as a distance metric, effectively suppressing outlier influence. An efficient iterative optimization algorithm, grounded in Fenchel conjugate theory and block coordinate update techniques, is developed to solve the model, and theoretical guarantees of convergence are provided. Comprehensive experiments on seven real-world image datasets demonstrate that LAGFC outperforms twelve state-of-the-art clustering methods across diverse scenarios in most cases, validating its accuracy and robustness.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".