Efficient spectral embedding representation approximation for large-scale data clustering
Bibliographic record
Abstract
Spectral clustering is a prevalent clustering method in which an affinity matrix is constructed based on all samples (the number is n ), leading to high computational complexity and making it infeasible for dealing with large-scale data directly. In this study, we introduce an Approximate Spectral Embedding Representation method (ASER). By employing an anchor-based strategy, the spectral embedding representation of the selected anchors (the number is m , m ≪ n ) is used to approximate the spectral embedding representation of the original samples. Unlike available methods that approximate the similarity matrix based on an anchor graph, we directly implement the approximation in the spectral embedding space. Moreover, the properties of the formed anchor graph are inherited from the original space to the spectral embedding space. The time complexity of conducting spectral clustering is significantly reduced from O ( n 3 ) to be linear with respect to n , without relying on any acceleration operations for eigenvalue decomposition. Experimental results on toy examples and benchmark datasets with large sizes demonstrate the effectiveness and efficiency of the proposed model.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".