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614,922
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 553 (21 self)
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This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task
The Foundations of CostSensitive Learning
 In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence
, 2001
"... This paper revisits the problem of optimal learning and decisionmaking when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrix is reasonable, and we show how to avoid the mistake of defining a cost matrix that is economically i ..."
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Cited by 403 (6 self)
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This paper revisits the problem of optimal learning and decisionmaking when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrix is reasonable, and we show how to avoid the mistake of defining a cost matrix that is economically
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
 California Institute of Technology, Pasadena
, 2008
"... Abstract. Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery alg ..."
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Cited by 770 (13 self)
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algorithm called CoSaMP that delivers the same guarantees as the best optimizationbased approaches. Moreover, this algorithm offers rigorous bounds on computational cost and storage. It is likely to be extremely efficient for practical problems because it requires only matrix–vector multiplies
Parallel Numerical Linear Algebra
, 1993
"... We survey general techniques and open problems in numerical linear algebra on parallel architectures. We first discuss basic principles of parallel processing, describing the costs of basic operations on parallel machines, including general principles for constructing efficient algorithms. We illust ..."
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Cited by 776 (23 self)
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We survey general techniques and open problems in numerical linear algebra on parallel architectures. We first discuss basic principles of parallel processing, describing the costs of basic operations on parallel machines, including general principles for constructing efficient algorithms. We
Computing Earth Mover’s Distance with Entry/Exit Fee Cost Matrix
"... Traditional minimum cost flow problems have been utilized as distance measures between two sets (assignment problem) or two distributions (transportation problem). The formal one is called the minimum difference of pair assignment (MDPA) distance and the later is called the earth mover’s distance (E ..."
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(EMD) which is the minimal amount of work that must be performed to transform one distribution into the other by moving distribution mass. If the distribution is represented as a histogram in b number of bins, the cost matrix is b×b square matrix. While generic algorithms such as Simplex method
An Interleaver Design Algorithm based on a Cost Matrix for Turbo Codes
"... Abstract  In this paper the design of interleavers for Turbo Codes is considered. The proposed algorithm is based on a Hamming weight cost matrix. It optimizes both the minimal distance of Turbo Codes and the passing of extrinsic information. Simulation results show that for short lengths these int ..."
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Abstract  In this paper the design of interleavers for Turbo Codes is considered. The proposed algorithm is based on a Hamming weight cost matrix. It optimizes both the minimal distance of Turbo Codes and the passing of extrinsic information. Simulation results show that for short lengths
Optimizing a Cost Matrix to Solve RareClass Biological Problems
"... Abstract — In a binary dataset, a rareclass problem occurs when one class of data (typically the class of interest) is far outweighed by the other. Such a problem is typically difficult to learn and classify and is quite common, especially among biological problems such as the identification of gen ..."
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of gene conversions. A multitude of solutions for this problem exist with varying levels of success. In this paper we present our solution, which involves using the MetaCost algorithm, a costsensitive “metaclassifier ” that requires a cost matrix to adjust the learning of an underlying classifier. Our
Optimizing the Cost Matrix for Approximate String Matching using Genetic Algorithms
 Pattern Recognition
, 1997
"... This paper describes a method for optimizing the cost matrix of any approximate string matching algorithm based on the Levenshtein distance. The method, which uses genetic algorithms, defines the problem formally as a discrimination between a set of classes. It is tested and evaluated using both syn ..."
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Cited by 3 (0 self)
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This paper describes a method for optimizing the cost matrix of any approximate string matching algorithm based on the Levenshtein distance. The method, which uses genetic algorithms, defines the problem formally as a discrimination between a set of classes. It is tested and evaluated using both
Reducing Annotation Effort on Unbalanced Corpus based on Cost Matrix
"... Annotated corpora play a significant role in many NLP applications. However, annotation by humans is timeconsuming and costly. In this paper, a high recall predictor based on a costsensitive learner is proposed as a method to semiautomate the annotation of unbalanced classes. We demonstrate the e ..."
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the effectiveness of our approach in the context of one form of unbalanced task: annotation of transcribed humanhuman dialogues for presence/absence of uncertainty. In two data sets, our costmatrix based method of uncertainty annotation achieved high levels of recall while maintaining acceptable levels
Targeting customer groups using gain and cost matrix; a marketing application
 in Data Mining for Marketing Applications (Working Notes), PKDD'2001
, 2001
"... Abstract. This paper introduces gaincost classi…cation matrix for targeting customer groups. A real life application was realized on a customer database of CCF Bank containing more than 400,000 instances. Results shows that scoring is almost insensitive to di¤erent costgain hypothesis; on the othe ..."
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Cited by 8 (0 self)
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Abstract. This paper introduces gaincost classi…cation matrix for targeting customer groups. A real life application was realized on a customer database of CCF Bank containing more than 400,000 instances. Results shows that scoring is almost insensitive to di¤erent costgain hypothesis
Results 1  10
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614,922