Finite sample guarantees for PCA in non-isotropic and data-dependent noise
Finite sample guarantees for PCA in non-isotropic and data-dependent noise
This work obtains novel finite sample guarantees for Principal Component Analysis (PCA). These hold even when the corrupting noise is non-isotropic, and a part (or all of it) is data-dependent. Because of the latter, in general, the noise and the true data are correlated. The results in this work are …