  This paper considers the problem of estimating a high-dimensional vector of
parameters $\boldsymbol{\theta} \in \mathbb{R}^n$ from a noisy observation. The
noise vector is i.i.d. Gaussian with known variance. For a squared-error loss
function, the James-Stein (JS) estimator is known to dominate the simple
maximum-likelihood (ML) estimator when the dimension $n$ exceeds two. The
JS-estimator shrinks the observed vector towards the origin, and the risk
reduction over the ML-estimator is greatest for $\boldsymbol{\theta}$ that lie
close to the origin. JS-estimators can be generalized to shrink the data
towards any target subspace. Such estimators also dominate the ML-estimator,
but the risk reduction is significant only when $\boldsymbol{\theta}$ lies
close to the subspace. This leads to the question: in the absence of prior
information about $\boldsymbol{\theta}$, how do we design estimators that give
significant risk reduction over the ML-estimator for a wide range of
$\boldsymbol{\theta}$?
  In this paper, we propose shrinkage estimators that attempt to infer the
structure of $\boldsymbol{\theta}$ from the observed data in order to construct
a good attracting subspace. In particular, the components of the observed
vector are separated into clusters, and the elements in each cluster shrunk
towards a common attractor. The number of clusters and the attractor for each
cluster are determined from the observed vector. We provide concentration
results for the squared-error loss and convergence results for the risk of the
proposed estimators. The results show that the estimators give significant risk
reduction over the ML-estimator for a wide range of $\boldsymbol{\theta}$,
particularly for large $n$. Simulation results are provided to support the
theoretical claims.
