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Constructing LinearSized Spectral Sparsification in AlmostLinear Time
"... We present the first almostlinear time algorithm for constructing linearsized spectral sparsification for graphs. This improves all previous constructions of linearsized spectral sparsification, which requires Ω(n2) time [1], [2], [3]. A key ingredient in our algorithm is a novel combination of t ..."
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We present the first almostlinear time algorithm for constructing linearsized spectral sparsification for graphs. This improves all previous constructions of linearsized spectral sparsification, which requires Ω(n2) time [1], [2], [3]. A key ingredient in our algorithm is a novel combination of two techniques used in literature for constructing spectral sparsification: Random sampling by effective resistance [4], and adaptive constructions based on barrier functions [1], [3]. Keywords algorithmic spectral graph theory; spectral sparsification I.
Fast Randomized Kernel Methods With Statistical Guarantees
"... One approach to improving the running time of kernelbased machine learning methods is to build a small sketch of the input and use it in lieu of the full kernel matrix in the machine learning task of interest. Here, we describe a version of this approach that comes with running time guarantees as w ..."
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One approach to improving the running time of kernelbased machine learning methods is to build a small sketch of the input and use it in lieu of the full kernel matrix in the machine learning task of interest. Here, we describe a version of this approach that comes with running time guarantees as well as improved guarantees on its statistical performance. By extending the notion of statistical leverage scores to the setting of kernel ridge regression, our main statistical result is to identify an importance sampling distribution that reduces the size of the sketch (i.e., the required number of columns to be sampled) to the effective dimensionality of the problem. This quantity is often much smaller than previous bounds that depend on the maximal degrees of freedom. Our main algorithmic result is to present a fast algorithm to compute approximations to these scores. This algorithm runs in time that is linear in the number of samples—more precisely, the running time is O(np2), where the parameter p depends only on the trace of the kernel matrix and the regularization parameter—and it can be applied to the matrix of feature vectors, without having to form the full kernel matrix. This is obtained via a variant of lengthsquared sampling that we adapt to the kernel setting in a way that is of independent interest. Lastly, we provide empirical results illustrating our theory, and we discuss how this new notion of the statistical leverage of a data point captures in a fine way the difficulty of the original statistical learning problem. 1