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… (More)
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Topic mentions per year

Topic mentions per year

2001-2015
01220012015

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2015
2015
Constrained image models based on linear dependence are commonly used in high dimensional imaging and computer vision to exploit… (More)
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Review
2015
Review
2015
In this talk I address one of the main challenges in high performance computing which is the increased cost of communication with… (More)
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2011
2011
We consider the synthesis problem of Compressed Sensing –given s and an M×n matrix A, extract from it an m × n submatrix Am… (More)
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2010
2010
This paper describes Householder reduction of a rectangular sparse matrix to small band upper triangular form Bk+1. Bk+1 is upper… (More)
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2009
2009
This paper describes Householder reduction of a rectangular sparse matrix to small band upper triangular form. Using block… (More)
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2006
2006
Low rank matrix approximations have many applications in different domains. In system theory it has been used in model reduction… (More)
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2003
2003
We consider the problem of clustering large document sets into disjoint groups or clusters. Our starting point is recent… (More)
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Highly Cited
2001
Highly Cited
2001
  • Dimitris A hlioptas, Frank M Sherryy
  • 2001
 
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