Add to ORKGThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-55).Low-rank matrix approximations are used in a significant number of applications. We present new algorithms for generating such approximations in a streaming fashion that expand upon recently discovered matrix sketching techniques. We test our approaches on real and synthetic data to explore runtime and accuracy performance. We apply our algorithms to the technique of Latent Semantic Indexing on a widely studied data set. We find our algorithms provide strong empirical results.by Timothy Matthew Galvin.M. Eng
In this thesis, we investigate how well we can reconstruct the best rank-? approximation of a large ...
Low-rank matrix approximation is an integral component of tools such as principal component analysis...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
In this paper, we revisit the problem of constructing a near-optimal rank k approximation of a matri...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
We prove that any real matrix A contains a subset of at most 4k/ɛ+2k log(k+1) rows whose span “conta...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the s...
In this paper we present a fast and accurate procedure called clustered low rank matrix approximatio...
This electronic version was submitted by the student author. The certified thesis is available in th...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
In many applications—latent semantic indexing, for example—it is required to obtain a reduced rank a...
This paper describes a suite of algorithms for constructing low-rank approximations of an input matr...
In this thesis, we investigate how well we can reconstruct the best rank-? approximation of a large ...
Low-rank matrix approximation is an integral component of tools such as principal component analysis...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
In this paper, we revisit the problem of constructing a near-optimal rank k approximation of a matri...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
We prove that any real matrix A contains a subset of at most 4k/ɛ+2k log(k+1) rows whose span “conta...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the s...
In this paper we present a fast and accurate procedure called clustered low rank matrix approximatio...
This electronic version was submitted by the student author. The certified thesis is available in th...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
In many applications—latent semantic indexing, for example—it is required to obtain a reduced rank a...
This paper describes a suite of algorithms for constructing low-rank approximations of an input matr...
In this thesis, we investigate how well we can reconstruct the best rank-? approximation of a large ...
Low-rank matrix approximation is an integral component of tools such as principal component analysis...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...