A Novel Semi-Supervised Dimensionality Reduction Framework
Why this work is in the frame
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Bibliographic record
Abstract
In pattern recognition, when a dataset contains multiple classes, and the structures of the classes are different, single manifold assumption can hardly guarantee the best classification performance. It is more reasonable to assume each class lies on a separate manifold. Here, the authors propose a novel framework of semisupervised dimensionality reduction for multimanifold learning. To address the issue of label insufficiency under the multimanifold assumption, they propose solving three challenging problems: clustering unlabeled samples into different manifolds using sparse manifold clustering, even when they are close to each other; predicting the label of image sets instead of a single sample by calculating the manifold-to-manifold distance; and constructing three kinds of graphs for each manifold to exploit more information from unlabeled samples. During the investigation, they demonstrated that most existing dimension-reduction methods based on manifold learning can be viewed as special cases of the proposed framework. Experimental results verify the advantages and effectiveness of this new framework.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it