Generalized Deep Embedded Fuzzy C-Means for Clustering High-Dimensional Data
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".