A fusion of principal component analysis and singular value decomposition based multivariate denoising algorithm for free induction decay transversal data
Bibliographic record
Abstract
The free induction decay (FID) transversal data determines the measurement accuracy of time-dependent geomagnetic fields, whereas the conservation of clean components and removal of noise cannot be easily achieved for this kind of data. Even though numerous techniques have been proven to be effective in improving the signal-to-noise ratio by filtering out frequency bands, how to efficiently reduce noise is still a crucial issue due to several restrictions, e.g., prior information requirement, stationary data assumption. To end this, a new multivariate algorithm based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD), namely, principal component analysis and decomposition (PCAD), was presented. This novel algorithm aims to reduce noise as well as cancel the interference of FID transversal data. Specifically, the PCAD algorithm is able to obtain the dominant principal components of the FID and that of the noise floor by PCA, in which an optimal number of subspaces could be retained via a cumulative percent of variance criterion. Furthermore, the PCA was combined with an SVD filter whose singular values corresponding to the interferences were identified, and then the noise was suppressed by nulling the corresponding singular values, which was able to achieve an optimum trade-off between the preservation of pure FID data and the denoising efficiency. Our proposed PCAD algorithm was compared with the widely used filter methods via extensive experiments on synthetic and real FID transversal data under different noise levels. The results demonstrated that this method can preserve the FID transversal data better and shows a significant improvement in noise suppression.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".