Regularized RLS Algorithm Based on Third-Order Tensor Decomposition
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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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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 it