Multiclass and Multilabel Classifications by Consensus and Complementarity-Based Multiview Latent Space Projection
Bibliographic record
Abstract
The fusion of multiview data sets, in which features of each sample are categorized into distinct groups, is increasingly important in the big data era. Successful multiview learning approaches have mechanisms to enforce consensus and/or complementarity among views. This article introduces a framework called the consensus and complementarity-based multiview latent space projection (MVLSP-2C) that enforces both principles simultaneously. Consensus is established by extracting and representing information shared by all views in a shared latent space, whereas complementarity among views is achieved by the representation in view-specific spaces. As the diversity of the multiview feature representation benefits classification performance, MVLSP-2C minimizes the similarity between the shared and view-specific representations, thereby improving diversity. The driving principle of MVLSP-2C is that the latent space representation is obtained by optimally projecting it to match the original feature space representation on a view-by-view basis. Unlike pairwise consensus methods that enforce consistency between two views, matching on a view-by-view basis allows extensions to settings with more than two views. A related and important advantage of this per-view matching design is that a class view can be readily incorporated to learn a supervised representation that facilitates subsequent classification. As the class view is added without an assumption on the exclusivity of classes, MVLSP-2C is equally applicable to multiclass single-label and multilabel classifications. MVLSP-2C further optimizes the integration of latent variables based on their correlation. Extensive experiments in multiclass and multiview image datasets show that MVLSP-2C produces more accurate classification results as compared to state-of-the-art methods.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".