Fdmclust: Functional data model-based clustering using approximation of probability density for a random function in a reproducing Kernel Hilbert space framework
Bibliographic record
Abstract
This paper proposes a new probability density approximation for functional random variables in the reproducing kernel Hilbert space (RKHS). Based on this approximation, a novel method for model-based clustering of functional data named Fdmclust is introduced. The previous study was based on the Gaussian assumption of the functional data due to the independence used for the covariance kernel. This assumption is not necessarily valid for general cases. The proposed method is applicable to both Gaussian and non-Gaussian functional data, utilizing the projection of functional data onto the Mercer kernel rather than the covariance kernel. To this end, PCA and ICA are used on the obtained projections for Gaussian and non-Gaussian functional random variables, respectively. The estimation of parameters is based on the EM-algorithm. The Fdmclust approach is evaluated on several simulated and real datasets, and the results confirm its efficiency.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".