Robust Scatter Matrix Estimation for Elliptical Distributions in Polynomial Time

• URL: http://arxiv.org/abs/2502.06564v1
• Date: Mon, 10 Feb 2025 15:31:57 GMT
• Title: Robust Scatter Matrix Estimation for Elliptical Distributions in Polynomial Time
• Authors: Gleb Novikov,
• Abstract summary: We design time algorithms that achieve dimension-independent error in Frobenius norm. Under a mild assumption on the eigenvalues of the scatter matrix $Sigma$, for every $t in mathbbN$, we design an estimator that, given $n = dO(t)$ samples, in time $nO(t)$ finds $hatSigma.
• Score: 2.311583680973075
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• Abstract: We study the problem of computationally efficient robust estimation of scatter matrices of elliptical distributions under the strong contamination model. We design polynomial time algorithms that achieve dimension-independent error in Frobenius norm. Our first result is a sequence of efficient algorithms that approaches nearly optimal error. Specifically, under a mild assumption on the eigenvalues of the scatter matrix $\Sigma$, for every $t \in \mathbb{N}$, we design an estimator that, given $n = d^{O(t)}$ samples, in time $n^{O(t)}$ finds $\hat{\Sigma}$ such that $ \Vert{\Sigma^{-1/2}\, ({\hat{\Sigma} - \Sigma})\, \Sigma^{-1/2}}\Vert_{\text{F}} \le O(t \cdot \varepsilon^{1-\frac{1}{t}})$, where $\varepsilon$ is the fraction of corruption. We do not require any assumptions on the moments of the distribution, while all previously known computationally efficient algorithms for robust covariance/scatter estimation with dimension-independent error rely on strong assumptions on the moments, such as sub-Gaussianity or (certifiable) hypercontractivity. Furthermore, under a stronger assumption on the eigenvalues of $\Sigma$ (that, in particular, is satisfied by all matrices with constant condition number), we provide a fast (sub-quadratic in the input size) algorithm that, given nearly optimal number of samples $n = \tilde{O}(d^2/\varepsilon)$, in time $\tilde{O}({nd^2 poly(1/\varepsilon)})$ finds $\hat{\Sigma}$ such that $\Vert\hat{\Sigma} - \Sigma\Vert_{\text{F}} \le O(\Vert{\Sigma}\Vert \cdot \sqrt{\varepsilon})$. Our approach is based on robust covariance estimation of the spatial sign (the projection onto the sphere of radius $\sqrt{d}$) of elliptical distributions.

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