Bibliographic record
Abstract
In this work, a discriminant correntropy analysis (DCA) method is proposed with application to multi-feature fusion. Benefiting from the joint strength of discriminant power and correntropy descriptor, not only is the discriminant representation explored but also the localized similarity is utilized to measure the structural relation between the given multiple features, generating a new multi-feature representation with high quality. Different from the most existing multi-feature fusion techniques, such as canonical correlation analysis (CCA) and kernel CCA (KCCA), the correntropy is used to reveal the intrinsic relation of input data sources instead of correlation. Moreover, unlike the traditional entropy-based algorithm (e.g., kernel entropy component analysis (KECA) method), DCA is able to be applied to multiple variables instead of a single data source only, enabling a more powerful tool for multi-feature fusion. The performance of the proposed DCA method is verified through experiments on audio emotion recognition and face recognition tasks. The results demonstrate DCA outperforms other deep neural network (DNN) and statistics machine learning (SML) based methods.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".