Singular vector and singular subspace distribution for the matrix denoising model
Zhigang Bao, Xiucai Ding, and Ke Wang
The Annals of Statistics · 2021 · Volume 49 · Issue 1
Abstract
In this paper, we study the matrix denoising model $Y=S+X$, where $S$ is a low rank deterministic signal matrix and $X$ is a random noise matrix, and both are $M\times n$. In the scenario that $M$ and $n$ are comparably large and the signals are supercritical, we study the fluctuation of the outlier singular vectors of $Y$, under fully general assumptions on the structure of $S$ and the distribution of $X$. More specifically, we derive the limiting distribution of angles between the principal singular vectors of $Y$ and their deterministic counterparts, the singular vectors of $S$. Further, we also derive the distribution of the distance between the subspace spanned by the principal singular vectors of $Y$ and that spanned by the singular vectors of $S$. It turns out that the limiting distributions depend on the structure of the singular vectors of $S$ and the distribution of $X$, and thus they are nonuniversal. Statistical applications of our results to singular vector and singular subspace inferences are also discussed.
Keywords
15B52, 60B20, 62G10, 62H10, 62H25, matrix denoising model, nonuniversality, Random matrix, signal-plus-noise model, singular subspace, singular vector
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