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| K-średnie z regularyzacją× | Regularyzowany model mieszaniny Gaussa× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2010 | 2000s–2010s |
| Twórca≠ | Witten, D. M. & Tibshirani, R. (sparse k-means formulation) | Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter) |
| Typ≠ | Regularized unsupervised clustering | Probabilistic clustering with regularization |
| Źródło pierwotne≠ | Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), 713–726. DOI ↗ | Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. DOI ↗ |
| Inne nazwy | sparse k-means, penalized k-means, regularized clustering, constrained k-means | Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM |
| Pokrewne≠ | 2 | 5 |
| Podsumowanie≠ | Regularized k-means extends standard k-means by adding a penalty term — most commonly an L1 (lasso-type) or L2 constraint — to the objective function. This discourages degenerate cluster solutions and, in the sparse variant introduced by Witten and Tibshirani (2010), simultaneously selects the features that drive cluster separation, making it especially valuable in high-dimensional settings where many features are irrelevant. | A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations. |
| ScholarGateZbiór danych ↗ |
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