Multilinear Compressed Sensing using Tensor Least Angle Regression (T-LARS)
Bibliographic record
Abstract
Multilinear compressed sensing generalizes the compressed sensing formulation to tensor signals, where the tensor signal is reconstructed using much fewer samples obtained in a sparse domain by solving a multilinear sparse coding problem. The Kronecker-OMP, a generalization of Orthogonal Matching Pursuit (OMP) solves the L0 constrained multilinear sparse least-squares problems. However, with the problem dimensions and the number of iterations, the space and computational cost of Kronecker-OMP increase in the polynomial order. Authors have previously developed a generalized least-angle regression(LARS), known as Tensor Least Angle Regression (T-LARS), with a lower asymptotic space and computational complexity than Kronecker-OMP to efficiently solve both L0 and L1 constrained multilinear sparse least-squares problems. In this paper, we used T-LARS to solve multilinear compressed sensing problems and compared the results with Kronecker-OMP, where the T-LARS is 56 times faster than Kronecker-OMP in reconstructing the 3D PET-CT images using compressed sensing samples.
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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 | 0.000 |
| 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 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".