Fast spectral clustering with self-weighted features
Bibliographic record
Abstract
As one of the mainstream clustering methods, the spectral clustering has aroused more and more attention recently because of its good performance, especially in nonlinear data sets. However, traditional spectral clustering models have high computational complexity. Meanwhile, most of these models fail in distinguishing the noisy and useful features in practice, which leads to the limitation of clustering performance. In this paper, we propose a new fast spectral clustering with self-weighted features (FSCSWF) to achieve good clustering performance through learning and assigning optimal weights for features in a low computational complexity. Specifically, the FSCSWF selects anchors from original samples, then learns the weights of features and the similarity between anchors and samples interactively in a local structure learning framework. This interactive learning makes the learnt similarity can better measure the relationship between anchors, and samples due to the optimal weights make the data points become more discriminative. Moreover, the connectivity constraint are embedded to make sure that the connected components of bipartite graph constructed by the learnt similarity can indicate clusters directly. In this way, the FSCSWF can achieve good clustering performance and has a low computational complexity, which is linear to the number of samples. Extensive experiments on synthetic and practical data sets illustrate the effectiveness and efficiency of the FSCSWF with respect to state-of-the-art methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".