The Experts below are selected from a list of 101244 Experts worldwide ranked by ideXlab platform
Mark D. Plumbley - One of the best experts on this subject based on the ideXlab platform.
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Gradient Polytope Faces Pursuit for large scale sparse recovery problems
2010Co-Authors: Aris Gretsistas, Ivan Damnjanovic, Mark D. PlumbleyAbstract:Polytope Faces Pursuit is a greedy algorithm that performs Basis Pursuit with similar order complexity to Orthogonal Matching Pursuit. The algorithm adds one basis Vector at a time and adopts a path-following approach based on the geometry of the polar polytope associated with the dual Linear Program. Its initial implementation uses the method of Cholesky factorization to update the Solution Vector at each step, which can be computationally expensive for solving large scale problems as it requires the succesive storage of large matrices. In this paper, we present a different approach using directional updates to estimate the Solution Vector at each time. The proposed method uses the gradient descent method, reducing the memory requirements and computational complexity. We demonstrate the application of this Gradient Polytope Faces Pursuit algorithm to a source separation problem.
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ICASSP - Gradient Polytope Faces Pursuit for large scale sparse recovery problems
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Aris Gretsistas, Ivan Damnjanovic, Mark D. PlumbleyAbstract:Polytope Faces Pursuit is a greedy algorithm that performs Basis Pursuit with similar order complexity to Orthogonal Matching Pursuit. The algorithm adds one basis Vector at a time and adopts a path-following approach based on the geometry of the polar polytope associated with the dual Linear Program. Its initial implementation uses the method of Cholesky factorization to update the Solution Vector at each step, which can be computationally expensive for solving large scale problems as it requires the succesive storage of large matrices. In this paper, we present a different approach using directional updates to estimate the Solution Vector at each time. The proposed method uses the gradient descent method, reducing the memory requirements and computational complexity. We demonstrate the application of this Gradient Polytope Faces Pursuit algorithm to a source separation problem.
Aris Gretsistas - One of the best experts on this subject based on the ideXlab platform.
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Gradient Polytope Faces Pursuit for large scale sparse recovery problems
2010Co-Authors: Aris Gretsistas, Ivan Damnjanovic, Mark D. PlumbleyAbstract:Polytope Faces Pursuit is a greedy algorithm that performs Basis Pursuit with similar order complexity to Orthogonal Matching Pursuit. The algorithm adds one basis Vector at a time and adopts a path-following approach based on the geometry of the polar polytope associated with the dual Linear Program. Its initial implementation uses the method of Cholesky factorization to update the Solution Vector at each step, which can be computationally expensive for solving large scale problems as it requires the succesive storage of large matrices. In this paper, we present a different approach using directional updates to estimate the Solution Vector at each time. The proposed method uses the gradient descent method, reducing the memory requirements and computational complexity. We demonstrate the application of this Gradient Polytope Faces Pursuit algorithm to a source separation problem.
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ICASSP - Gradient Polytope Faces Pursuit for large scale sparse recovery problems
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Aris Gretsistas, Ivan Damnjanovic, Mark D. PlumbleyAbstract:Polytope Faces Pursuit is a greedy algorithm that performs Basis Pursuit with similar order complexity to Orthogonal Matching Pursuit. The algorithm adds one basis Vector at a time and adopts a path-following approach based on the geometry of the polar polytope associated with the dual Linear Program. Its initial implementation uses the method of Cholesky factorization to update the Solution Vector at each step, which can be computationally expensive for solving large scale problems as it requires the succesive storage of large matrices. In this paper, we present a different approach using directional updates to estimate the Solution Vector at each time. The proposed method uses the gradient descent method, reducing the memory requirements and computational complexity. We demonstrate the application of this Gradient Polytope Faces Pursuit algorithm to a source separation problem.
Malik Magdon-ismail - One of the best experts on this subject based on the ideXlab platform.
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A note on sparse least-squares regression
Information Processing Letters, 2014Co-Authors: Christos Boutsidis, Malik Magdon-ismailAbstract:We compute a sparse Solution to the classical least-squares problem min"[email protected][email protected]?"2, where A is an arbitrary matrix. We describe a novel algorithm for this sparse least-squares problem. The algorithm operates as follows: first, it selects columns from A, and then solves a least-squares problem only with the selected columns. The column selection algorithm that we use is known to perform well for the well studied column subset selection problem. The contribution of this article is to show that it gives favorable results for sparse least-squares as well. Specifically, we prove that the Solution Vector obtained by our algorithm is close to the Solution Vector obtained via what is known as the ''SVD-truncated regularization approach''.
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A note on sparse least-squares regression
arXiv: Data Structures and Algorithms, 2013Co-Authors: Christos Boutsidis, Malik Magdon-ismailAbstract:We compute a \emph{sparse} Solution to the classical least-squares problem $\min_x||A x -b||,$ where $A$ is an arbitrary matrix. We describe a novel algorithm for this sparse least-squares problem. The algorithm operates as follows: first, it selects columns from $A$, and then solves a least-squares problem only with the selected columns. The column selection algorithm that we use is known to perform well for the well studied column subset selection problem. The contribution of this article is to show that it gives favorable results for sparse least-squares as well. Specifically, we prove that the Solution Vector obtained by our algorithm is close to the Solution Vector obtained via what is known as the "SVD-truncated regularization approach".
Christos Boutsidis - One of the best experts on this subject based on the ideXlab platform.
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A note on sparse least-squares regression
Information Processing Letters, 2014Co-Authors: Christos Boutsidis, Malik Magdon-ismailAbstract:We compute a sparse Solution to the classical least-squares problem min"[email protected][email protected]?"2, where A is an arbitrary matrix. We describe a novel algorithm for this sparse least-squares problem. The algorithm operates as follows: first, it selects columns from A, and then solves a least-squares problem only with the selected columns. The column selection algorithm that we use is known to perform well for the well studied column subset selection problem. The contribution of this article is to show that it gives favorable results for sparse least-squares as well. Specifically, we prove that the Solution Vector obtained by our algorithm is close to the Solution Vector obtained via what is known as the ''SVD-truncated regularization approach''.
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A note on sparse least-squares regression
arXiv: Data Structures and Algorithms, 2013Co-Authors: Christos Boutsidis, Malik Magdon-ismailAbstract:We compute a \emph{sparse} Solution to the classical least-squares problem $\min_x||A x -b||,$ where $A$ is an arbitrary matrix. We describe a novel algorithm for this sparse least-squares problem. The algorithm operates as follows: first, it selects columns from $A$, and then solves a least-squares problem only with the selected columns. The column selection algorithm that we use is known to perform well for the well studied column subset selection problem. The contribution of this article is to show that it gives favorable results for sparse least-squares as well. Specifically, we prove that the Solution Vector obtained by our algorithm is close to the Solution Vector obtained via what is known as the "SVD-truncated regularization approach".
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On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions
arXiv: Data Structures and Algorithms, 2011Co-Authors: Christos BoutsidisAbstract:We describe two algorithms for computing a sparse Solution to a least-squares problem where the coefficient matrix can have arbitrary dimensions. We show that the Solution Vector obtained by our algorithms is close to the Solution Vector obtained via the truncated SVD approach.
Ivan Damnjanovic - One of the best experts on this subject based on the ideXlab platform.
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Gradient Polytope Faces Pursuit for large scale sparse recovery problems
2010Co-Authors: Aris Gretsistas, Ivan Damnjanovic, Mark D. PlumbleyAbstract:Polytope Faces Pursuit is a greedy algorithm that performs Basis Pursuit with similar order complexity to Orthogonal Matching Pursuit. The algorithm adds one basis Vector at a time and adopts a path-following approach based on the geometry of the polar polytope associated with the dual Linear Program. Its initial implementation uses the method of Cholesky factorization to update the Solution Vector at each step, which can be computationally expensive for solving large scale problems as it requires the succesive storage of large matrices. In this paper, we present a different approach using directional updates to estimate the Solution Vector at each time. The proposed method uses the gradient descent method, reducing the memory requirements and computational complexity. We demonstrate the application of this Gradient Polytope Faces Pursuit algorithm to a source separation problem.
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ICASSP - Gradient Polytope Faces Pursuit for large scale sparse recovery problems
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Aris Gretsistas, Ivan Damnjanovic, Mark D. PlumbleyAbstract:Polytope Faces Pursuit is a greedy algorithm that performs Basis Pursuit with similar order complexity to Orthogonal Matching Pursuit. The algorithm adds one basis Vector at a time and adopts a path-following approach based on the geometry of the polar polytope associated with the dual Linear Program. Its initial implementation uses the method of Cholesky factorization to update the Solution Vector at each step, which can be computationally expensive for solving large scale problems as it requires the succesive storage of large matrices. In this paper, we present a different approach using directional updates to estimate the Solution Vector at each time. The proposed method uses the gradient descent method, reducing the memory requirements and computational complexity. We demonstrate the application of this Gradient Polytope Faces Pursuit algorithm to a source separation problem.