A Stitch in Time Saves Nine: Progressive Information Bottleneck for Incremental Multiview Clustering
Bibliographic record
Abstract
Incremental multiview clustering (IMVC) leverages consistent information between historical and new views to benefit the clustering task. However, existing IMVC approaches ignore the redundant information in individual views, leading to an accumulation of irrelevance. Besides, with the continuous arrivals of new views, the knowledge learned from historical views is often forgotten, which hinders the learning models from achieving long-term dependencies across incremental views. In this study, we propose a novel progressive information bottleneck (PIB), which is capable of removing redundant information in a timely manner and selectively updating historical knowledge based on information gain of new views. Specifically, to facilitate the knowledge transfer from historical views to incoming one, an information-aware knowledge library is built to store the representative samples of historical views. With the emergence of new views, we first devise a matrix-based mutual information (MI) constraint on an encoder to compress redundant information, which facilitates the training of a neural network with analyzable gradients and obtain a compact yet discriminative representation. Then, a dual-selective updating strategy is proposed to preserve historical knowledge in time when it contributes more to the information gain of the knowledge library than the new view. Finally, relevant samples to the new view in knowledge library are migrated to maximize the cross-level consistency between historical and new views. To the best of our knowledge, this is the first work that designs a gradient-analyzable MI measurement for incremental multiview learning and employs information gain to guide the selective update of the knowledge library. Empirical evaluations on six benchmark datasets show that our method outperforms state-of-the-art baseline methods by an average of 8.1%, 7.6%, and 7.8% on clustering accuracy (ACC), normalized mutual information (NMI), and adjusted Rand index (ARI) metrics, respectively.
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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 | 0.007 |
| 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 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".