Low Rank Matrix Approximations are essential tools in the application of kernel methods to large-scale learning problems. Kernel methods (forâ€¦Â (More)

Semantic Scholar uses AI to extract papers important to this topic.

2015

2015

- Evan Levine, Tiffany Jou
- 2015

Constrained image models based on linear dependence are commonly used in high dimensional imaging and computer vision to exploitâ€¦Â (More)

Is this relevant?

Review

2015

Review

2015

- Laura Grigori
- SC '15
- 2015

In this talk I address one of the main challenges in high performance computing which is the increased cost of communication withâ€¦Â (More)

Is this relevant?

2011

2011

- Anatoli Juditsky, Fatma KilinÃ§-Karzan, Arkadi Nemirovski
- SIAM J. Matrix Analysis Applications
- 2011

We consider the synthesis problem of Compressed Sensing â€“given s and an MÃ—n matrix A, extract from it an m Ã— n submatrix Amâ€¦Â (More)

Is this relevant?

2010

2010

- George Howell
- 2010

This paper describes Householder reduction of a rectangular sparse matrix to small band upper triangular form Bk+1. Bk+1 is upperâ€¦Â (More)

Is this relevant?

2009

2009

- George Howell
- 2009

This paper describes Householder reduction of a rectangular sparse matrix to small band upper triangular form. Using blockâ€¦Â (More)

Is this relevant?

2006

2006

Low rank matrix approximations have many applications in different domains. In system theory it has been used in model reductionâ€¦Â (More)

Is this relevant?

2003

2003

We consider the problem of clustering large document sets into disjoint groups or clusters. Our starting point is recentâ€¦Â (More)

Is this relevant?

Highly Cited

2001

Highly Cited

2001

- Dimitris A hlioptas, Frank M Sherryy
- 2001

Â

Is this relevant?