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| Pääkomponenttianalyysi× | Robustti kovarianssimenetelmä (MCD)× | |
|---|---|---|
| Tieteenala≠ | Koneoppiminen | Tilastotiede |
| Menetelmäperhe≠ | Machine learning | Regression model |
| Syntyvuosi≠ | 2002 | 1999 |
| Kehittäjä≠ | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) |
| Tyyppi≠ | Unsupervised dimensionality reduction | Robust multivariate location-scatter estimator |
| Alkuperäislähde≠ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ | Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223. DOI ↗ |
| Rinnakkaisnimet | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform | minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD) |
| Liittyvät≠ | 3 | 4 |
| Tiivistelmä≠ | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. | Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation. |
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