Quantification and localization of features in time-frequency plane
Bibliographic record
Abstract
Many feature extraction techniques in literature have studied data representation, but most techniques do not explicitly investigate the feature localization aspect. This is one of the first works in which signals have been transformed to matrices using positive time-frequency transform, and matrix decomposition and representation techniques such as PCA, ICA, and NMF have been applied on these matrices to study the feature representation and localization issues. To estimate each techniquespsila localization, we propose a localization measurement method. We also construct a non stationary synthetic signal which resembles major characteristics of real world signals, and then apply the feature extraction techniques on a simple time-frequency distribution (TFD) of this signal. The localization results show that matrix factorization 1-D deconvolution (NMF1D) offers the most localized features with 99.6% localization. In addition, we demonstrate that under different number of basis components and noisy conditions, NMF1D offers the most robust localization.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".