Kernel Cross-Modal Factor Analysis for Information Fusion With Application to Bimodal Emotion Recognition
Bibliographic record
Abstract
In this paper, we investigate kernel based methods for multimodal information analysis and fusion. We introduce a novel approach, kernel cross-modal factor analysis, which identifies the optimal transformations that are capable of representing the coupled patterns between two different subsets of features by minimizing the Frobenius norm in the transformed domain. The kernel trick is utilized for modeling the nonlinear relationship between two multidimensional variables. We examine and compare with kernel canonical correlation analysis which finds projection directions that maximize the correlation between two modalities, and kernel matrix fusion which integrates the kernel matrices of respective modalities through algebraic operations. The performance of the introduced method is evaluated on an audiovisual based bimodal emotion recognition problem. We first perform feature extraction from the audio and visual channels respectively. The presented approaches are then utilized to analyze the cross-modal relationship between audio and visual features. A hidden Markov model is subsequently applied for characterizing the statistical dependence across successive time segments, and identifying the inherent temporal structure of the features in the transformed domain. The effectiveness of the proposed solution is demonstrated through extensive experimentation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".