A new criterion for selection of non‐zero loadings for sparse principal component analysis (<scp>SPCA</scp>)
Bibliographic record
Abstract
Abstract Sparse principal component analysis (SPCA) has recently emerged as an approach aimed at producing compact principal component loadings by suppressing spurious values and thus overcoming some limitations of the traditional principal component analysis (PCA). This paper proposes a fault detection and diagnosis (FDD) method based on SPCA; in this approach, the number of non‐zero loadings (NNZL) of SPCAs is selected based on both the false alarm rate (FAR) and the fault detection rate (FDR). The criterion is to have lower FAR and higher FDR. This new feature makes SPCA better suited for FDD, which is demonstrated by comparing its performance with that of three other methods for finding loadings. The overall FDD performances of both PCA and SPCA‐based techniques are illustrated using the benchmark continuous stirred tank heater (CSTH) process. The results show that the PCs derived based on the proposed criterion has a better fault diagnosis ability.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".