Class-wise deep dictionaries for EEG classification
Why this work is in the frame
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Bibliographic record
Abstract
In this work we propose a classification framework called class-wise deep dictionary learning (CWDDL). For each class, multiple levels of dictionaries are learnt using features from the previous level as inputs (for first level the input is the raw training sample). It is assumed that the cascaded dictionaries form a basis for expressing test samples for that class. Based on this assumption sparse representation based classification is employed. Benchmarking experiments have been carried out on some deep learning datasets (MNIST and its variations, CIFAR and SVHN); our proposed method has been compared with Deep Belief Network (DBN), Stacked Autoencoder, Convolutional Neural Net (CNN) and Label Consistent KSVD (dictionary learning). We find that our proposed method yields better results than these techniques and requires much smaller run-times. The technique is applied for Brain Computer Interface (BCI) classification problems using EEG signals. For this problem our method performs significantly better than Convolutional Deep Belief Network(CDBN).
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it