Support Multimode Tensor Machine for Multiple Classification on Industrial Big Data
Bibliographic record
Abstract
Supervised machine learning algorithms, especially classification algorithms, have been widely used in data analysis of industrial big data. Among them, the support vector machine (SVM) has achieved great success in the binary classification of some areas like image processing, computer vision, and pattern recognition. However, an SVM cannot achieve the desirable classification results for heterogeneous and high-dimensional data generated from thousands of industrial sensors in physical environments, because the traditional vector-based and feature-aligned SVM algorithm may result in loss of structural information and rich context information. Although the support tensor machine (STM) has extended the traditional vector-based SVM to tensor space, it fails to deal with multiple classification problems. Therefore, designing a general multiple classification algorithm for heterogeneous and high-dimensional data is a challenging but promising topic. To achieve this goal, this article presents a support multimode tensor machine (SMTM) algorithm by applying the multimode product to generalize the formulation of the STM. Furthermore, this article presents an efficient algorithm to train the parameters. Experiments conducted on various data sets validate the better performance of the SMTM over other algorithms in the multiple classification and imply the potential of the proposed model for multiple classification on industrial big data.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".