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Sublineartime algorithms
 In Oded Goldreich, editor, Property Testing, volume 6390 of Lecture Notes in Computer Science
, 2010
"... In this paper we survey recent (up to end of 2009) advances in the area of sublineartime algorithms. 1 ..."
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Cited by 20 (2 self)
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In this paper we survey recent (up to end of 2009) advances in the area of sublineartime algorithms. 1
Sublinear Time Approximate Clustering
, 2001
"... Clustering is of central importance in a number of disciplines including Machine Learning, Statistics, and Data Mining. This paper has two foci: (1) It describes how existing algorithms for clustering can benefit from simple sampling techniques arising from work in statistics [Pol84]. (2) It motivat ..."
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Cited by 46 (2 self)
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Clustering is of central importance in a number of disciplines including Machine Learning, Statistics, and Data Mining. This paper has two foci: (1) It describes how existing algorithms for clustering can benefit from simple sampling techniques arising from work in statistics [Pol84]. (2) It motivates and introduces a new model of clustering that is in the spirit of the "PAC (probably approximately correct)" learning model, and gives examples of efficient PACclustering algorithms.
Facility location in sublinear time
 In 32nd International Colloquium on Automata, Languages, and Programming
, 2005
"... Abstract. In this paper we present a randomized constant factor approximation algorithm for the problem of computing the optimal cost of the metric Minimum Facility Location problem, in the case of uniform costs and uniform demands, and in which every point can open a facility. By exploiting the fac ..."
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Cited by 13 (3 self)
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of our algorithm is sublinear with respect to the input size. We consider also the general version of the metric Minimum Facility Location problem and we show that there is no o(n 2)time algorithm, even a randomized one, that approximates the optimal solution to within any factor. This result can
Coloring in Sublinear Time
 Proceedings of the ESA 1997, Springer Lecture Notes in Computer Science 1284
, 1997
"... We will present an algorithm, based on SAtechniques ... ..."
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Cited by 3 (1 self)
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We will present an algorithm, based on SAtechniques ...
Sublinear Time Orthogonal Tensor Decomposition *
"... Abstract A recent work (Wang et. al., NIPS 2015) gives the fastest known algorithms for orthogonal tensor decomposition with provable guarantees. Their algorithm is based on computing sketches of the input tensor, which requires reading the entire input. We show in a number of cases one can achiev ..."
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achieve the same theoretical guarantees in sublinear time, i.e., even without reading most of the input tensor. Instead of using sketches to estimate inner products in tensor decomposition algorithms, we use importance sampling. To achieve sublinear time, we need to know the norms of tensor slices, and we
Combinatorial sublineartime fourier algorithms
, 2009
"... We study the problem of estimating the best k term Fourier representation for a given frequencysparse signal (i.e., vector) A of length N ≫ k. More explicitly, we investigate how to deterministically identify k of the largest magnitude frequencies of Â, and estimate their coefficients, in polynomia ..."
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Cited by 34 (5 self)
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, in polynomial(k, log N) time. Randomized sublinear time algorithms which have a small (controllable) probability of failure for each processed signal exist for solving this problem [24, 25]. In this paper we develop the first known deterministic sublinear time sparse Fourier Transform algorithm which
Approximating Semidefinite Programs in Sublinear Time
"... In recent years semidefinite optimization has become a tool of major importance in various optimization and machine learning problems. In many of these problems the amount of data in practice is so large that there is a constant need for faster algorithms. In this work we present the first sublinear ..."
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Cited by 9 (3 self)
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sublinear time approximation algorithm for semidefinite programs which we believe may be useful for such problems in which the size of data may cause even linear time algorithms to have prohibitive running times in practice. We present the algorithm and its analysis alongside with some theoretical lower
Finding Cycles and Trees in Sublinear Time
, 2011
"... We present sublineartime (randomized) algorithms for finding simple cycles of length at least k ≥ 3 and treeminors in boundeddegree graphs. The complexity of these algorithms is related to the distance of the graph from being Ckminor free (resp., free from having the corresponding treeminor). I ..."
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Cited by 1 (1 self)
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We present sublineartime (randomized) algorithms for finding simple cycles of length at least k ≥ 3 and treeminors in boundeddegree graphs. The complexity of these algorithms is related to the distance of the graph from being Ckminor free (resp., free from having the corresponding tree
Flexible music retrieval in sublinear time
 IN PROC. 10TH PRAGUE STRINGOLOGY CONFERENCE (PSC'05)
, 2005
"... Music sequences can be treated as texts in order to perform music retrieval tasks on them. However, the text search problems that result from this modeling are unique to music retrieval. Up to date, several approaches derived from classical string matching have been proposed to cope with the new s ..."
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Cited by 7 (4 self)
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Music sequences can be treated as texts in order to perform music retrieval tasks on them. However, the text search problems that result from this modeling are unique to music retrieval. Up to date, several approaches derived from classical string matching have been proposed to cope with the new search problems, yet each problem had its own algorithms. In this paper we show that a technique recently developed for multipattern approximate string matching is flexible enough to be successfully extended to solve many different music retrieval problems, as well as combinations thereof not addressed before. We show that the resulting algorithms are close to optimal and much better than existing approaches in many practical cases.
Results 1  10
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820