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10,695
Sparse Greedy Matrix Approximation for Machine Learning
, 2000
"... In kernel based methods such as Regularization Networks large datasets pose signi cant problems since the number of basis functions required for an optimal solution equals the number of samples. We present a sparse greedy approximation technique to construct a compressed representation of the ..."
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Cited by 222 (10 self)
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In kernel based methods such as Regularization Networks large datasets pose signi cant problems since the number of basis functions required for an optimal solution equals the number of samples. We present a sparse greedy approximation technique to construct a compressed representation
Generalized nonnegative matrix approximations
, 2005
"... Abstract. In this report we present new algorithms for nonnegative matrix approximation (NMA), commonly known as the NMF problem. Our methods improve upon the wellknown methods of Lee & Seung [19] for both the Frobenius norm as well the KullbackLeibler divergence versions of the problem. For ..."
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Cited by 1 (1 self)
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Abstract. In this report we present new algorithms for nonnegative matrix approximation (NMA), commonly known as the NMF problem. Our methods improve upon the wellknown methods of Lee & Seung [19] for both the Frobenius norm as well the KullbackLeibler divergence versions of the problem
Generalized rankconstrained matrix approximations
, 2006
"... In this paper we give an explicit solution to the rank constrained matrix approximation in Frobenius norm, which is a generalization of the classical approximation of an m × n matrix A by a matrix of rank k at most. ..."
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Cited by 17 (5 self)
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In this paper we give an explicit solution to the rank constrained matrix approximation in Frobenius norm, which is a generalization of the classical approximation of an m × n matrix A by a matrix of rank k at most.
Fast Computation of Low Rank Matrix Approximations
, 2001
"... In many practical applications, given an m n matrix A it is of interest to nd an approximation to A that has low rank. We introduce a technique that exploits spectral structure in A to accelerate Orthogonal Iteration and Lanczos Iteration, the two most common methods for computing such approximat ..."
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Cited by 165 (5 self)
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In many practical applications, given an m n matrix A it is of interest to nd an approximation to A that has low rank. We introduce a technique that exploits spectral structure in A to accelerate Orthogonal Iteration and Lanczos Iteration, the two most common methods for computing
Local LowRank Matrix Approximation
"... Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of lowrank. We propose a new matrix approximation model where we assume instead that the matrix is ..."
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Cited by 5 (1 self)
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Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of lowrank. We propose a new matrix approximation model where we assume instead that the matrix
Uniform sampling for matrix approximation
 In Proceedings of the 6th Annual Conference on Innovations in Theoretical Computer Science (ITCS
, 2015
"... ar ..."
Interest Zone Matrix Approximation
, 2011
"... We present an algorithm for low rank approximation of matrices where only some of the entries in the matrix are taken into consideration. This algorithm appears in recent literature under different names, where it is described as an EM based algorithm that maximizes the likelihood for the missing en ..."
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We present an algorithm for low rank approximation of matrices where only some of the entries in the matrix are taken into consideration. This algorithm appears in recent literature under different names, where it is described as an EM based algorithm that maximizes the likelihood for the missing
Results 1  10
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10,695