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Similarity search in high dimensions via hashing
, 1999
"... The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image dat ..."
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Cited by 641 (10 self)
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to 20, searching in kd trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than bruteforce linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather
Hashing Advantages of Hashing
"... •Given a query point q, return the points closest to q •Useful for finding similar objects in database. •Brute force linear search is not practical in massive databases. Applications: optical character recognition, knearest neighbor algorithm, contentbased image retrieval, maximum likelihood decod ..."
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•Given a query point q, return the points closest to q •Useful for finding similar objects in database. •Brute force linear search is not practical in massive databases. Applications: optical character recognition, knearest neighbor algorithm, contentbased image retrieval, maximum likelihood
Depth first search and linear graph algorithms
 SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
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Cited by 1406 (19 self)
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The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components
A LinearTime Heuristic for Improving Network Partitions
, 1982
"... An iterative mincut heuristic for partitioning networks is presented whose worst case computation time, per pass, grows linearly with the size of the network. In practice, only a very small number of passes are typically needed, leading to a fast approximation algorithm for mincut partitioning. To d ..."
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Cited by 524 (0 self)
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An iterative mincut heuristic for partitioning networks is presented whose worst case computation time, per pass, grows linearly with the size of the network. In practice, only a very small number of passes are typically needed, leading to a fast approximation algorithm for mincut partitioning
Local Search: Is bruteforce avoidable?
"... Many local search algorithms are based on searching in the kexchange neighborhood. This is the set of solutions that can be obtained from the current solution by exchanging at most k elements. As a rule of thumb, the larger k is, the better are the chances of finding an improved solution. However, ..."
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Cited by 16 (2 self)
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, for inputs of size n, a naïve bruteforce search of the kexchange neighborhood requires nO(k) time, which is not practical even for very small values of k. We show that for several classes of sparse graphs, like planar graphs, graphs of bounded vertex degree and graphs excluding some fixed graph as a minor
Scatter/Gather: A Clusterbased Approach to Browsing Large Document Collections
, 1992
"... Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably ..."
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Cited by 777 (12 self)
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improve retrieval. We argue that these problems arise only when clustering is used in an attempt to improve conventional search techniques. However, looking at clustering as an information access tool in its own right obviates these objections, and provides a powerful new access paradigm. We present a
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 775 (21 self)
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Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information
An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database
 J. Am. Soc. Mass Spectrom
, 1994
"... A method to correlate the uninterpreted tandem mass spectra of peptides produced under low energy (lo50 eV) collision conditions with amino acid sequences in the Genpept database has been developed. In this method the protein database is searched to identify linear amino acid sequences within a mas ..."
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Cited by 944 (19 self)
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A method to correlate the uninterpreted tandem mass spectra of peptides produced under low energy (lo50 eV) collision conditions with amino acid sequences in the Genpept database has been developed. In this method the protein database is searched to identify linear amino acid sequences within a
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
, 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
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Cited by 539 (17 self)
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Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a
The use of MMR, diversitybased reranking for reordering documents and producing summaries
 In SIGIR
, 1998
"... jadeQcs.cmu.edu Abstract This paper presents a method for combining queryrelevance with informationnovelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in reranking retrieved docum ..."
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Cited by 768 (14 self)
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systems. However, the clearest advantage is demonstrated in constructing nonredundant multidocument summaries, where MMR results are clearly superior to nonMMR passage selection. 2 Maximal Marginal Relevance Most modem IR search engines produce a ranked list of retrieved documents ordered by declining
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