IDECF: Improved Deep Embedding Clustering With Deep Fuzzy Supervision
Bibliographic record
Abstract
Deep clustering algorithms utilize a deep neural network to map data points in a lower-dimensional space which is more suitable for clustering task. Recent algorithms employ autoencoder to jointly learn a lower-dimensional space (aka latent space) and perform data clustering through minimizing a clustering loss. These algorithms suffer from the fact that the true cluster assignments are unknown because of the unsupervised nature of the task. Thus, they adopt a self-training strategy and estimate the true cluster labels using the algorithm parameters; while the true parameters’ value is unknown at the problem outset. To address this difficulty, we propose a deep clustering technique, called IDECF, whereby the true cluster assignments are estimated using an individual deep fully connected network (FCM-Net) which takes its input from the latent space of an autoencoder. The proposed IDECF is trained in an end-to-end manner by minimizing a linear combination of reconstruction loss and clustering loss. Experimental results on benchmark datasets demonstrate the viability and effectiveness of the proposed 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".