Add to ORKGThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2007.Includes bibliographical references (p. 51-52).Can one compute a low-dimensional representation of any given data by looking only at its small sample, chosen cleverly on the fly? Motivated by the above question, we consider the problem of low-rank matrix approximation: given a matrix A..., one wants to compute a rank-k matrix (where k 1) to given points. We generalize our sampling techniques and prove similar sampling-based dimension reduction results for subspace approximation. However, the proof is geometric.by Amit Jayant Deshpande.Ph.D
Abstract Low-rank matrix approximation has applications in many fields, such as 3D reconstruction fr...
A matrix algorithm runs at sublinear cost if the number of arithmetic operations involved is far few...
We present two new results for the problem of approximating a given real m by n matrix A by a rank-k...
University of Minnesota Ph.D. disseration. May 2014. Major: Computer Science. Advisor: Youcef Saad. ...
We study three fundamental problems of Linear Algebra, lying in the heart of various Machine Learnin...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
Extracting low dimensional structure from high dimensional data arises in many applications such as ...
Recent advances in matrix approximation have seen an emphasis on randomization techniques in which t...
In the subspace approximation problem, given m points in R^{n} and an integer k <= n, the goal is to...
1 Introduction Let the rows of a matrix be points in a high-dimensionalspace. It is often of interes...
The two most popular unsupervised learning problems are k-Clustering and Low-Rank Approximation. Con...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
textDue to the rapidly increasing dimensionality of modern datasets many classical approximation alg...
We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provi...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...
Abstract Low-rank matrix approximation has applications in many fields, such as 3D reconstruction fr...
A matrix algorithm runs at sublinear cost if the number of arithmetic operations involved is far few...
We present two new results for the problem of approximating a given real m by n matrix A by a rank-k...
University of Minnesota Ph.D. disseration. May 2014. Major: Computer Science. Advisor: Youcef Saad. ...
We study three fundamental problems of Linear Algebra, lying in the heart of various Machine Learnin...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
Extracting low dimensional structure from high dimensional data arises in many applications such as ...
Recent advances in matrix approximation have seen an emphasis on randomization techniques in which t...
In the subspace approximation problem, given m points in R^{n} and an integer k <= n, the goal is to...
1 Introduction Let the rows of a matrix be points in a high-dimensionalspace. It is often of interes...
The two most popular unsupervised learning problems are k-Clustering and Low-Rank Approximation. Con...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
textDue to the rapidly increasing dimensionality of modern datasets many classical approximation alg...
We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provi...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...
Abstract Low-rank matrix approximation has applications in many fields, such as 3D reconstruction fr...
A matrix algorithm runs at sublinear cost if the number of arithmetic operations involved is far few...
We present two new results for the problem of approximating a given real m by n matrix A by a rank-k...