A new extension to kernel entropy component analysis for image-based authentication systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We introduce Feature Dependent Kernel Entropy Component\nAnalysis (FDKECA) as a new extension to Kernel\nEntropy Component Analysis (KECA) for data transformation\nand dimensionality reduction in Image-based recognition\nsystems such as face and finger vein recognition. FDKECA\nreveals structure related to a new mapping space,\nwhere the most optimized feature vectors are obtained and\nused for feature extraction and dimensionality reduction.\nIndeed, the proposed method uses a new space, which is feature\nwisely dependent and related to the input data space, to\nobtain significant PCA axes. We show that FDKECA produces\nstrikingly different transformed data sets compared to\nKECA and PCA. Furthermore a new spectral clustering algorithm\nutilizing FDKECA is developed which has positive\nresults compared to the previously used ones. More precisely,\nFDKECA clustering algorithm has both more time\nefficiency and higher accuracy rate than previously used\nmethods. Finally, we compared our method with three\nwell-known data transformation methods, namely Principal\nComponent Analysis (PCA), Kernel Principal Component\nAnalysis (KPCA), and Kernel Entropy Component Analysis\n(KECA) confirming that it outperforms all these direct competitors\nand as a result, it is revealed that FDKECA can be\nconsidered a useful alternative for PCA-based recognition\nalgorithms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.121 | 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 it