Deep tensor decomposition: A survey
Bibliographic record
Abstract
Tensor decomposition (TD) has been recognized as an effective technique for multilinear dimensionality reduction and feature extraction for decades. However, traditional TD approaches often struggle to capture complex hierarchical structures and nonlinear relationships in high-dimensional datasets. For instance, in biomedical settings, disease groups may naturally contain subgroups or exhibit hierarchical structures; mechanistic interactions among diseases, drugs and targets often demonstrate nonlinearity. To address these challenges, a new paradigm, deep tensor decomposition (deep TD) has recently emerged inspired by the success of deep learning. Deep TD techniques can be mainly divided into two categories: linear and nonlinear deep TD. Linear deep TD exploits the layered structure of deep neural networks (DNNs) to recursively factorize factor matrices obtained from the classic TD enabling feature extraction at multiple levels of granularity. Nonlinear deep TD leverages the expressive power of DNNs to capture nonlinear correlations within the data. Despite rapid progress, there remains no unified treatment of deep TD methods. In this survey, we provide a comprehensive review of deep TD models, together with the deep learning training schemes for TD, and applications of deep TD models. Finally, we discuss open challenges and outline promising directions for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".