  Let $A\in\mathbb{R}^{m\times n}$ be a matrix of rank $r$ with singular value
decomposition (SVD) $A=\sum_{k=1}^r\sigma_k (u_k\otimes v_k),$ where
$\{\sigma_k, k=1,\ldots,r\}$ are singular values of $A$ (arranged in a
non-increasing order) and $u_k\in {\mathbb R}^m, v_k\in {\mathbb R}^n,
k=1,\ldots, r$ are the corresponding left and right orthonormal singular
vectors. Let $\tilde{A}=A+X$ be a noisy observation of $A,$ where
$X\in\mathbb{R}^{m\times n}$ is a random matrix with i.i.d. Gaussian entries,
$X_{ij}\sim\mathcal{N}(0,\tau^2),$ and consider its SVD
$\tilde{A}=\sum_{k=1}^{m\wedge
n}\tilde{\sigma}_k(\tilde{u}_k\otimes\tilde{v}_k)$ with singular values
$\tilde{\sigma}_1\geq\ldots\geq\tilde{\sigma}_{m\wedge n}$ and singular vectors
$\tilde{u}_k,\tilde{v}_k,k=1,\ldots, m\wedge n.$
  The goal of this paper is to develop sharp concentration bounds for linear
forms $\langle \tilde u_k,x\rangle, x\in {\mathbb R}^m$ and $\langle \tilde
v_k,y\rangle, y\in {\mathbb R}^n$ of the perturbed (empirical) singular vectors
in the case when the singular values of $A$ are distinct and, more generally,
concentration bounds for bilinear forms of projection operators associated with
SVD. In particular, the results imply upper bounds of the order
$O\biggl(\sqrt{\frac{\log(m+n)}{m\vee n}}\biggr)$ (holding with a high
probability) on $$\max_{1\leq i\leq
m}\big|\big<\tilde{u}_k-\sqrt{1+b_k}u_k,e_i^m\big>\big|\ \ {\rm and} \ \
\max_{1\leq j\leq n}\big|\big<\tilde{v}_k-\sqrt{1+b_k}v_k,e_j^n\big>\big|,$$
where $b_k$ are properly chosen constants characterizing the bias of empirical
singular vectors $\tilde u_k, \tilde v_k$ and $\{e_i^m,i=1,\ldots,m\},
\{e_j^n,j=1,\ldots,n\}$ are the canonical bases of $\mathbb{R}^m, {\mathbb
R}^n,$ respectively.
