Robust Regularized Recursive Least-Squares Algorithm Based on Third-Order Tensor Decomposition
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Bibliographic record
Abstract
The decomposition-based adaptive filtering algorithms have recently gained increasing interest due to their capability to reduce the parameter space. In this context, the third-order tensor (TOT) decomposition technique reformulates the conventional approach that involves a single (usually long) adaptive filter by using a combination of three shorter filters via the Kronecker product. This leads to a twofold gain in terms of both performance and complexity. Thus, it can be applied efficiently when operating with more complex algorithms, like the recursive least-squares (RLS) approach. In this paper, we develop an RLS-TOT algorithm with improved robustness features due to a novel regularization method that considers the contribution of the external noise and the so-called model uncertainties (which are related to the system). Simulation results obtained in the framework of echo cancelation support the performance of the proposed algorithm, which outperforms the existing RLS-TOT counterparts, as well as the conventional RLS algorithm that uses the specific regularization technique.
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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 | 0.001 |
| 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