Clustering Analysis via Deep Generative Models With Mixture Models
Bibliographic record
Abstract
Clustering is a fundamental problem that frequently arises in many fields, such as pattern recognition, data mining, and machine learning. Although various clustering algorithms have been developed in the past, traditional clustering algorithms with shallow structures cannot excavate the interdependence of complex data features in latent space. Recently, deep generative models, such as autoencoder (AE), variational AE (VAE), and generative adversarial network (GAN), have achieved remarkable success in many unsupervised applications thanks to their capabilities for learning promising latent representations from original data. In this work, first we propose a novel clustering approach based on both Wasserstein GAN with gradient penalty (WGAN-GP) and VAE with a Gaussian mixture prior. By combining the WGAN-GP with VAE, the generator of WGAN-GP is formulated by drawing samples from the probabilistic decoder of VAE. Moreover, to provide more robust clustering and generation performance when outliers are encountered in data, a variant of the proposed deep generative model is developed based on a Student's-t mixture prior. The effectiveness of our deep generative models is validated though experiments on both clustering analysis and samples generation. Through the comparison with other state-of-art clustering approaches based on deep generative models, the proposed approach can provide more stable training of the model, improve the accuracy of clustering, and generate realistic samples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".