Empirical mode decomposition based sparse dictionary learning with application to signal classification
Bibliographic record
Abstract
This paper will present a novel empirical framework for dictionary learning where the dictionary is learned from the data to be analyzed, rather than using a pre-defined basis. A dictionary formation and learning algorithm is presented, which learns sparse dictionaries, where sparsity is understood in terms of the small number of dictionary atoms compared to the signal dimensions. An initial dictionary is formed using training signals of different classes, where the dictionary atoms consist of intrinsic mode functions obtained as a result of decomposing the training signals using empirical mode decomposition. A dictionary learning algorithm trains this dictionary which results in a significant reduction in the size of the learned dictionary. The learned dictionary can be applied to signal classification, whereby coefficients of orthogonal projections of test signals against the learned dictionary are used as features to classify the test signals into different classes. We also show that the learned dictionary allows calculation of the coefficient vector based on sparse representation of test signals, which can also be used as a feature vector. Although the framework is not formulated as reconstructive, or combined reconstructive and discriminative dictionary learning, its efficacy in signal classification is demonstrated using real-life EEG signals.
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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 | 0.004 |
| 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) | 0.001 | 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".