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Recently several results appeared that show significant reduction in time for matrix multiplication, singular value decomposition as well as linear (2) regression, all based on data dependent random sampling. Our key idea is that low dimensional embeddings can be used to eliminate data dependence and provide more versatile, linear time pass efficient matrix(More)
Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fast-food, an approximation that accelerates such computation significantly. Key to Fastfood is the(More)
Least squares approximation is a technique to find an approximate solution to a system of linear equations that has no exact solution. Methods dating back to Gauss and Legendre find a solution in O(nd 2) time, where n is the number of constraints and d is the number of variables. We present two randomized algorithms that provide very accurate relative-error(More)
Personalized PageRank expresses link-based page quality around user-selected pages in a similar way as PageRank expresses quality over the entire web. Existing personalized PageRank algorithms can, however, serve online queries only for a restricted choice of pages. In this paper we achieve full personalization by a novel algorithm that precomputes a(More)
Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists or other means of human intervention. We assume that spammed(More)
Personalized PageRank expresses link-based page quality around user selected pages. The only previous personalized PageRank algorithm that can serve on-line queries for an unrestricted choice of pages on large graphs is our Monte Carlo algorithm [WAW 2004]. In this paper we achieve unrestricted personalization by combining rounding and randomized sketching(More)