Orthogonal Tensor Recovery Based on Non-Convex Regularization and Rank Estimation
Bibliographic record
Abstract
In this paper, a method for orthogonal tensor recovery based on non-convex regularization and rank estimation (OTRN-RE) is proposed, which aims to accurately recover the low-rank and sparse components of the tensor. Specifically, a new low-rank tensor decomposition algorithm is designed, which can efficiently establish the equivalence between the rank of a large tensor before decomposition and the rank of the coefficient tensor after decomposition. The large tensor is decomposed into a small standard orthogonal tensor and another coefficient tensor, and a generalized non-convex regularization is used to inscribe the low rank of the coefficient tensor. Meanwhile, a new rank estimation strategy is developed to dynamically adjust the size of small orthogonal tensors and coefficient tensors. Experimental results on image denoising and salient object detection tasks confirm the state-of-the-art performance of the proposed method in terms of denoising capability and computational speed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".