Generalized Deep Embedded Fuzzy C-Means for Clustering High-Dimensional Data
Why this work is in the frame
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Bibliographic record
Abstract
Clustering is one of the fundamental techniques of machine learning. Its integration with deep neural networks allows for extracting robust feature representations and yielding better clustering results. Deep-embedded clustering algorithms employ external information to form auxiliary and target distributions minimized via KL divergence. This study introduces a Generalized Deep Embedded Fuzzy C-Means (GDeeFCM) algorithm that learns both feature representations and cluster assignments at the same time. The principal advantage of GDeeFCM is using the objective function of the clustering algorithm, FCM in our case, as the loss function to update the encoder weights and clusters center simultaneously without the need to adapt information from external sources. It is worth mentioning that FCM is selected for this study, but any clustering algorithm can be employed. Our model's performance is compared with similar structures that utilize t-SNE for soft label assignments and KL Divergence as the loss function. Experimental results on four image datasets demonstrate the effectiveness of our algorithm.
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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 | 0.001 | 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 | 0.002 | 0.003 |
| 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 it