A deep support vector clustering algorithm for unsupervised and semi‐supervised learning
Bibliographic record
Abstract
Abstract As a widely carried out task in data‐driven applications, clustering relies on good data representation. Since deep neural networks are powerful tools for the analysis of clustering‐friendly representations, certain combinations of clustering and deep models have been explored in the literature. Yet, only limited improvement has been achieved for real data with complex structures such as positive and unlabelled (PU) data. In this article we propose an unsupervised clustering model, called the deep support vector clustering (dSVC). The method combines a deep autoencoder neural network with hinge loss, and is further extended to binary semi‐supervised PU data learning. Theoretical results are established for label recovery and novelty detection using a large‐margin classifier. Intensive numerical experiments on multiple datasets of both high and low dimension validate the efficiency of the proposed approach. We found that the proposed approach constructs clusters in a manner opposite to the popular generative adversarial network (GAN) model.
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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".