A new extension to kernel entropy component analysis for image-based authentication systems
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".