TT-TSVD: A Multi-modal Tensor Train Decomposition with Its Application in Convolutional Neural Networks for Smart Healthcare
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Bibliographic record
Abstract
Smart healthcare systems are generating a large scale of heterogenous high-dimensional data with complex relationships. It is hard for current methods to analyze such high-dimensional healthcare data. Specifically, the traditional data reduction methods can not keep the correlation among different modalities of data objects, while the latest methods based on tensor singular value decomposition are not effective for data reduction, although they can keep the correlation. This article presents a tensor train-tensor singular value decomposition (TT-TSVD) algorithm for data reduction. Particularly, the presented algorithm balances the correlation-preservation ability of modalities and data reduction ability by combining the advantages of the train structure of the tensor train decomposition and the association relationship between the tensor singular value decomposition retention mode. Extensive experiments are conducted on the convolutional neural network and the results clearly show that the presented algorithm performs effectively for data reduction with a low-loss classification accuracy; what is more, classification accuracy on medical image dataset has been improved a little.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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