Spiked multiplicative random matrices and principal components

In this paper, we study the eigenvalues and eigenvectors of the spiked invariant multiplicative models when the randomness is from Haar matrices. We establish the limits of the outlier eigenvalues lambda i and the generalized components (⟨v, ui⟩ for any deterministic vector v) of the outlier eigenvectors ui with optimal convergence rates. Moreover, we prove that the non-outlier eigenvalues stick with those of the unspiked matrices and the non-outlier eigenvectors are delocalized. The results also hold near the so-called BBP transition and for degenerate spikes. On one hand, our results can be regarded as a refinement of the counterparts of Belinschi et al. (2017) under additional regularity conditions. On the other hand, they can be viewed as an analog of Ding and Yang (2021) by replacing the random matrix with i.i.d. entries with Haar random matrix.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).