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Sparse Greedy Matrix Approximation for Machine Learning

by Alex J. Smola, Bernhard Schölkopf , 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 ..."
Abstract - Cited by 222 (10 self) - Add to MetaCart
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

by Suvrit Sra, Suvrit Sra , 2005
"... Abstract. In this report we present new algorithms for non-negative matrix approximation (NMA), commonly known as the NMF problem. Our methods improve upon the well-known methods of Lee & Seung [19] for both the Frobenius norm as well the Kullback-Leibler divergence versions of the problem. For ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract. In this report we present new algorithms for non-negative matrix approximation (NMA), commonly known as the NMF problem. Our methods improve upon the well-known methods of Lee & Seung [19] for both the Frobenius norm as well the Kullback-Leibler divergence versions of the problem

Generalized rank-constrained matrix approximations

by Shmuel Friedland, Anatoli Torokhti , 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. ..."
Abstract - Cited by 17 (5 self) - Add to MetaCart
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

by Dimitris Achlioptas, Frank McSherry , 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 ..."
Abstract - Cited by 165 (5 self) - Add to MetaCart
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 Low-Rank Matrix Approximation

by Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer
"... 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 low-rank. We propose a new matrix approximation model where we assume instead that the matrix is ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
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 low-rank. We propose a new matrix approximation model where we assume instead that the matrix

Uniform sampling for matrix approximation

by Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford - In Proceedings of the 6th Annual Conference on Innovations in Theoretical Computer Science (ITCS , 2015
"... ar ..."
Abstract - Cited by 5 (4 self) - Add to MetaCart
Abstract not found

Interest Zone Matrix Approximation

by Gil Shabat, Amir Averbuch , 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

Clustering based on matrix approximation:

by Tao Li
"... a unifying view ..."
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a unifying view

THE CP-MATRIX APPROXIMATION PROBLEM

by Jinyan Fan, Anwa Zhou
"... ar ..."
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and finite matrix approximations

by Maciej Trzetrzelewskia
"... ar ..."
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