PCA in High Dimensions : An orientation
When the data are high dimensional, widely used multivariate statistical methods such as principal component analysis can behave in unexpected ways. In settings where the dimension of the observations is comparable to the sample size, upward bias in sample eigenvalues and inconsistency of sample eig...
| Publié dans: | Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. - 1998. - 106(2018), 8 vom: 11. Aug., Seite 1277-1292 |
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| Format: | Article en ligne |
| Langue: | English |
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2018
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| Accès à la collection: | Proceedings of the IEEE. Institute of Electrical and Electronics Engineers |
| Sujets: | Journal Article Marchenko-Pastur distribution Tracy-Widom law phase transition phenomena principal component analysis random matrix theory spiked covariance model |
| Accès en ligne |
Volltext |