Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Robustná analýza hlavných komponent (RPCA)× | Analýza hlavných komponentov× | |
|---|---|---|
| Odbor≠ | Štatistika | Strojové učenie |
| Rodina≠ | Regression model | Machine learning |
| Rok vzniku≠ | 2011 | 2002 |
| Tvorca≠ | Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Typ≠ | Robust dimensionality reduction / matrix decomposition | Unsupervised dimensionality reduction |
| Pôvodný zdroj≠ | Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Ďalšie názvy | RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA) | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Príbuzné | 3 | 3 |
| Zhrnutie≠ | Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part. | 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. |
| ScholarGateDátová sada ↗ |
|
|