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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".