Efficient Algorithms for Maximum Covariance Analysis of Datasets with Many Variables and Fewer Realizations: A Revisit
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Bibliographic record
Abstract
Fast and reliable algorithms for maximum covariance analysis (MCA) are investigated. The traditional algorithm based on the direct singular value decomposition (SVD) of a covariance matrix is computationally expensive for large datasets. An alternate algorithm proposed in this study uses the QR factorization technique to reduce the computational burden of the MCA of datasets with many variables and fewer realizations. It is slightly slower but more reliable, as indicated in an example, than an existing alternate algorithm based on the eigenvalue decomposition of a quadruple matrix product. It is faster than another alternate algorithm that uses the principal component analyses of the datasets as the preliminary step of the MCA.
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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 | 0.002 |
| 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) | 0.000 | 0.000 |
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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