Ensemble Minimum Sum of Squared Similarities sampling for Nyström-based spectral clustering
Bibliographic record
Abstract
Spectral clustering is a powerful approach for clustering, with applications across multiple disciplines, including bioinformatics. However, the way its computational complexity scales limits its application in analyzing large datasets. This complexity can be reduced using the Nyström method, which subsamples the input data in a way that preserves its representational diversity. There are different established strategies for subsampling, yet they may have performance limitations for certain complex datasets. This paper we propose an alternative to those methods, introducing a new sampling procedure called Ensemble Minimum Sum of Squared Similarities (EMS3). We further improve on this method by using weight mixtures in subsample selection, yielding more accurate low-rank approximations than existing ensemble Nyström methods. We also provide a theoretical analysis of the upper error bound of the EMS3 algorithm, and demonstrate its performance in comparison to the leading spectral clustering methods that use Nyström sampling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".