The Annals of Statistics

Low rank estimation of smooth kernels on graphs

Vladimir Koltchinskii and Pedro Rangel

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Let $(V,A)$ be a weighted graph with a finite vertex set $V$, with a symmetric matrix of nonnegative weights $A$ and with Laplacian $\Delta$. Let $S_{\ast}: V\times V\mapsto{\mathbb{R}}$ be a symmetric kernel defined on the vertex set $V$. Consider $n$ i.i.d. observations $(X_{j},X_{j}',Y_{j})$, $j=1,\ldots,n$, where $X_{j}$, $X_{j}'$ are independent random vertices sampled from the uniform distribution in $V$ and $Y_{j}\in{\mathbb{R}}$ is a real valued response variable such that ${\mathbb{E}}(Y_{j}|X_{j},X_{j}')=S_{\ast}(X_{j},X_{j}')$, $j=1,\ldots,n$. The goal is to estimate the kernel $S_{\ast}$ based on the data $(X_{1},X_{1}',Y_{1}),\ldots,(X_{n},X_{n}',Y_{n})$ and under the assumption that $S_{\ast}$ is low rank and, at the same time, smooth on the graph (the smoothness being characterized by discrete Sobolev norms defined in terms of the graph Laplacian). We obtain several results for such problems including minimax lower bounds on the $L_{2}$-error and upper bounds for penalized least squares estimators both with nonconvex and with convex penalties.

Article information

Ann. Statist., Volume 41, Number 2 (2013), 604-640.

First available in Project Euclid: 26 April 2013

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Zentralblatt MATH identifier

Primary: 62J99: None of the above, but in this section 62H12: Estimation 60B20: Random matrices (probabilistic aspects; for algebraic aspects see 15B52) 60G15: Gaussian processes

Matrix completion low-rank matrix estimation optimal error rate minimax error bound matrix Lasso nuclear norm graph Laplacian discrete Sobolev norm


Koltchinskii, Vladimir; Rangel, Pedro. Low rank estimation of smooth kernels on graphs. Ann. Statist. 41 (2013), no. 2, 604--640. doi:10.1214/13-AOS1088.

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