Graph and Sparse-Based Robust Nonnegative Block Value Decomposition for Clustering
Bibliographic record
Abstract
In this paper, we first investigate the nonnegative block value decomposition (NBVD) approach through graph-based representation for clustering called G-NBVD. Then, we propose our three-step graph and sparse-based robust NBVD (GSR-NBVD) via robust NBVD (R-NBVD) framework. The robustness to outliers is obtained by converting the Frobenius norm of error function to the$\ell _{2,1}$-norm for NBVD structure that compensates the effect of samples that are not conforming to NBVD. To exploit the connection between the learning matrix and its corresponding coefficients through sparse representation, we enforce the sparse constraints on the middle matrix in the R-NBVD framework called SR-NBVD. To enhance the geometrical information from data space to the new space, we add a term to our objective minimization function through a regularized graph representation compact form called GSR-NBVD. Then, we prove the convergence of our proposed methods and show a visualization of the effectiveness of G-NBVD and GSR-NBVD step-by-step. Finally, we evaluate our proposed clustering methods over different kinds of data sets. The experimental results confirm that our methods outperforms several state-of-the-art methods through different metrics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".