Tim Sullivan

Junior Professor in Applied Mathematics:
Risk and Uncertainty Quantification

Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity

Preprint: Computing with dense kernel matrices at near-linear cost

Florian Schäfer, Houman Owhadi and I have just uploaded a preprint of our latest paper, “Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity” to the arXiv. This paper builds upon the probabilistic-numerical ideas of “gamblets” (elementary gables upon the solution of a PDE) introduced by Owhadi (2016) to provide near-linear cost \(\varepsilon\)-approximate compression, inversion and principal component analysis of dense kernel matrices, the entries of which come from Green's functions of suitable differential operators.

Abstract. Dense kernel matrices \(\Theta \in \mathbb{R}^{N \times N}\) obtained from point evaluations of a covariance function \(G\) at locations \(\{x_{i}\}_{1 \leq i \leq N}\) arise in statistics, machine learning, and numerical analysis. For covariance functions that are Green's functions elliptic boundary value problems and approximately equally spaced sampling points, we show how to identify a subset \(S \subset \{ 1,\dots, N \} \times \{ 1,\dots,N \}\), with \(\#S = O(N \log(N)\log^{d}(N/\varepsilon))\), such that the zero fill-in block-incomplete Cholesky decomposition of \(\Theta_{i,j} 1_{(i,j) \in S}\) is an \(\varepsilon\)-approximation of \(\Theta\). This block-factorisation can provably be obtained in \(O(N \log^{2}(N)(\log(1/\varepsilon)+\log^{2}(N))^{4d+1})\) complexity in time. Numerical evidence further suggests that element-wise Cholesky decomposition with the same ordering constitutes an \(O(N \log^{2}(N) \log^{2d}(N/\varepsilon))\) solver. The algorithm only needs to know the spatial configuration of the \(x_{i}\) and does not require an analytic representation of \(G\). Furthermore, an approximate PCA with optimal rate of convergence in the operator norm can be easily read off from this decomposition. Hence, by using only subsampling and the incomplete Cholesky decomposition, we obtain at nearly linear complexity the compression, inversion and approximate PCA of a large class of covariance matrices. By inverting the order of the Cholesky decomposition we also obtain a near-linear-time solver for elliptic PDEs.

Published on Tuesday 13 June 2017 at 07:00 UTC #publication #preprint #prob-num