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Low Rank Matrix Approximations

Low Rank Matrix Approximations are essential tools in the application of kernel methods to large-scale learning problems. Kernel methods (for… 
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Related topics

Related topics

14 relations
Algorithmic efficiencyCholesky decompositionFeature vectorGaussian process

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
Exploiting Non-local Low Rank Structure in Image Reconstruction
Constrained image models based on linear dependence are commonly used in high dimensional imaging and computer vision to exploit… 
Review
2015
Review
2015
Fast and robust communication avoiding algorithms: current status and future prospects
In this talk I address one of the main challenges in high performance computing which is the increased cost of communication with… 
2011
2011
Extended abstract
2010
2010
Lazy Householder Decomposition of Sparse Matrices
This paper describes Householder reduction of a rectangular sparse matrix to small band upper triangular form Bk+1. Bk+1 is upper… 
2009
2009
UB k+1 V Block Sparse Householder Decomposition ∗
This paper describes Householder reduction of a rectangular sparse matrix to small band upper triangular form. Using block… 
2006
2006
TOWARDS A NEW MATRIX DECOMPOSITION
Low rank matrix approximations have many applications in different domains. In system theory it has been used in model reduction… 
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