An Optimal Edge-weighted Graph Semantic Correlation Framework for Multi-view Feature Representation Learning
Bibliographic record
Abstract
In this article, we present an optimal edge-weighted graph semantic correlation (EWGSC) framework for multi-view feature representation learning. Different from most existing multi-view representation methods, local structural information and global correlation in multi-view feature spaces are exploited jointly in the EWGSC framework, leading to a new and high-quality multi-view feature representation. Specifically, a novel edge-weighted graph model is first conceptualized and developed to preserve local structural information in each of the multi-view feature spaces. Then, the explored structural information is integrated with a semantic correlation algorithm, labeled multiple canonical correlation analysis (LMCCA), to form a powerful platform for effectively exploiting local and global relations across multi-view feature spaces jointly. We then theoretically verified the relation between the upper limit on the number of projected dimensions and the optimal solution to the multi-view feature representation problem. To validate the effectiveness and generality of the proposed framework, we conducted experiments on five datasets of different scales, including visual-based (University of California Irvine (UCI) iris database, Olivetti Research Lab (ORL) face database, and Caltech 256 database), text-image-based (Wiki database), and video-based (Ryerson Multimedia Lab (RML) audio-visual emotion database) examples. The experimental results show the superiority of the proposed framework on multi-view feature representation over state-of-the-art algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".