Variable Length K-SVD: A New Dictionary Learning Approach and Multi-Stage OMP Method for Sparse Representation
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Bibliographic record
Abstract
The Sparse representation research field and applications have been rapidly growing during the past 15 years. The use of overcomplete dictionaries in sparse representation has gathered extensive attraction. Sparse representation was followed by the concept of adapting dictionaries to the input data (dictionary learning). The K-SVD is a well-known dictionary learning approach and is widely used in different applications. In this thesis, a novel enhancement to the K-SVD algorithm is proposed which creates a learnt dictionary with a specific number of atoms adapted for the input data set. To increase the efficiency of the orthogonal matching pursuit (OMP) method, a new sparse representation method is proposed which applies a multi-stage strategy to reduce computational cost. A new phase included DCT (PI-DCT) dictionary is also proposed which significantly reduces the blocking artifact problem of using the conventional DCT. The accuracy and efficiency of the proposed methods are then compared with recent approaches that demonstrate the promising performance of the methods proposed in this thesis.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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