Adaptive Lp-Norm Regularized Sparse Representation for Human Activity Recognition in Coal Mines
Bibliographic record
Abstract
This paper aims to overcome the lack of in-depth exploration into the intrinsic geometry of human activities. For this purpose, a generalized adaptive Lp-norm regularized sparse representation (ARSR) approach was proposed for human activity recognition, which preserves the model adaptability through the adaptive Lp-norm regularization. In essence, the proposed method applies sparse representation to human activity recognition, turning it into a new optimization problem. In addition, the problem was solved by the iterative-shrinkage-thresholding algorithm. Specifically, the sparse representation learned by the ARSR algorithm was introduced into the support vector machine (SVM) classifier. Then, several experiments were conducted on coal-mining datasets for human activity identification. The experimental results revealed that the proposed algorithm is superior to the current sparse representation algorithms like the standard L1-norm regularized sparse representation algorithm. The research findings shed new light on the human activity recognition in coal mines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".