Information Fusion VIA Optimized KECA with Application to Audio Emotion Recognition
Bibliographic record
Abstract
As a recent proposed information fusion tool, Kernel Entropy Component Analysis (KECA) has attracted more attentions from the research communities of multimedia. It utilizes descriptor of entropy estimation and achieves improved performance for information fusion. However, KECA roughly reduces to sorting the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis (KPCA), without extracting the optimal features retaining more entropy of the input data. In this paper, a novel approach Optimized Kernel Entropy Components Analysis (OKECA) is introduced to information fusion, which can be considered as an alternative method to KECA for information fusion. Since OKECA explicitly extracts the optimal features that retain most informative content, it leads to improving the final performance or classification accuracy. To demonstrate the effectiveness of the proposed solution, experiments are conducted on Ryerson Multimedia Lab (RML) and eNTERFACE emotion datasets. Experimental results show that the proposed solution outperforms the existing methods based on the similar principles, and the Deep Learning (DL) based method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".