We study the algorithmic problem of sparse mean estimation in the presence of
adversarial outliers. Specifically, the algorithm observes a \emph{corrupted}
set of samples from $\mathcal{N}(\mu,\mathbf{I}_d)$, where the unknown mean
$\mu \in \mathbb{R}^d$ is constrained to be $k$-sparse. A series of prior works
has developed efficient algorithms for robust sparse mean estimation with
sample complexity $\mathrm{poly}(k,\log d, 1/\epsilon)$ and runtime $d^2
\mathrm{poly}(k,\log d,1/\epsilon)$, where $\epsilon$ is the fraction of
contamination. In particular, the fastest runtime of existing algorithms is
quadratic ($\Omega(d^2)$), which can be prohibitive in high dimensions. This
quadratic barrier in the runtime stems from the reliance of these algorithms on
the sample covariance matrix, which is of size $d^2$. Our main contribution is
an algorithm for robust sparse mean estimation which runs in
\emph{subquadratic} time using $\mathrm{poly}(k,\log d,1/\epsilon)$ samples. We
also provide analogous results for robust sparse PCA. Our results build on
algorithmic advances in detecting weak correlations, a generalized version of
the light-bulb problem by Valiant.

稀疏均值估计算法在存在对抗性异常值的情况下的研究，提出了一种在次二次时间内运行的鲁棒稀疏均值估计算法，并在检测弱相关性方面取得了算法进展。