Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling
Bibliographic record
Abstract
<p>Current approaches to improve the pattern recognition performance mainly focus on either extracting non-stationary</p> <p>and discriminant features of each class, or employing complex and nonlinear feature classifiers. However, little</p> <p>attention has been paid to the integration of these two approaches. Combining non-stationary feature analysis with</p> <p>complex feature classifiers, this article presents a novel direction to enhance the discriminatory power of pattern</p> <p>recognition methods. This approach, which is based on a fusion of non-stationary feature analysis with clustering</p> <p>techniques, proposes an algorithm to adaptively identify the feature vectors according to their importance in</p> <p>representing the patterns of discrimination. Non-stationary feature vectors are extracted using a non-stationary</p> <p>method based on time–frequency distribution and non-negative matrix factorization. The clustering algorithms</p> <p>including the K-means and self-organizing tree maps are utilized as unsupervised clustering methods followed by a</p> <p>supervised labeling. Two labeling methods are introduced: hard and fuzzy labeling. The article covers in detail the</p> <p>formulation of the proposed discriminant feature clustering method. Experiments performed with pathological</p> <p>speech classification, T-wave alternans evaluation from the surface electrocardiogram, audio scene analysis, and</p> <p>telemonitoring of Parkinson’s disease problems produced desirable results. The outcome demonstrates the benefits</p> <p>of non-stationary feature fusion with clustering methods for complex data analysis where existing approaches do not</p> <p>exhibit a high performance.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".