An efficient algorithm for Parafac with uncorrelated mode‐A components applied to large <i>I</i> × <i>J</i> × <i>K</i> data sets with <i>I</i> >> <i>JK</i>
Bibliographic record
Abstract
Abstract Harshman and Lundy (Comput. Stat. Data Anal. 1994; 18: 39–72) described an option in the Parafac algorithm for constraining one or more component matrices to have uncorrelated columns. In cases where an algorithm using this constraint is to be applied to a three‐way array that has too many entries in one mode, the algorithm is computationally impractical. The present paper describes two modifications of their algorithm to handle the multiplications involving the large mode efficiently for cases where the product of the sizes of the other two modes is smaller than that of the larger mode. By means of a simulation study, it is demonstrated that these procedures (which are similarly efficient) are much more efficient than the original algorithm. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".