Regularized RLS Algorithm Based on Third-Order Tensor Decomposition
Bibliographic record
Abstract
Adaptive filters characterized by long length impulse responses are required in many real-world system identification applications, among which echo cancellation is a classical example. In these scenarios, fast converging algorithms would be desirable, like those belonging to the recursive least-squares (RLS) family. However, the high computational complexity of such solutions represents a significant limitation in practice. The recently developed RLS algorithm based on a third-order tensor (TOT) decomposition, namely RLS-TOT, overcomes this drawback, by using a combination of three shorter adaptive filters instead of a single long-length one. In this paper, we further develop a regularized RLS-TOT algorithm, with improved robustness features in noisy conditions. The proposed solution is based on the regularized least-squares optimization criterion, while the regularization parameters are chosen in an optimal manner, depending on the signal-to-noise ratio. Simulations performed in an echo cancellation scenario support the performance gain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".