Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Primārā komponentu analīze× | LASSO regresija× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2002 | 1996 |
| Autors≠ | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) | Tibshirani, R. |
| Tips≠ | Unsupervised dimensionality reduction | Regularized linear regression (L1 penalty) |
| Pirmavots≠ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Citi nosaukumi | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | 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. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. |
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