Online incremental higher-order partial least squares regression for fast reconstruction of motion trajectories from tensor streams
Why this work is in the frame
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Bibliographic record
Abstract
The higher-order partial least squares (HOPLS) is considered as the state-of-the-art tensor-variate regression modeling for predicting a tensor response from a tensor input. However, the standard HOPLS can quickly become computationally prohibitive or merely impossible, especially when huge and time-evolving tensorial streams arrive over time in dynamic application environments. In this paper, we present a computationally efficient online tensor regression algorithm, namely incremental higher-order partial least squares (IHOPLS), for adapting HOPLS to the setting of infinite time-dependent tensor streams. By incrementally clustering the projected latent variables in latent space and summarizing the previous data, IHOPLS is able to recursively update the projection matrices and core tensors over time, resulting in greatly reduced costs in terms of both memory and running time while maintaining high prediction accuracy. To show the effectiveness and scalability of our approach for large databases, we apply IHOPLS to two real-life applications as reconstruction of 3D motion trajectories from video and ECoG streaming signals.
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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) | 0.001 | 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