Matrix Approximation and Projective Clustering via Iterative Sampling
We present two new results for the problem of approximating a given real m by n matrix A by a rank-k matrix D, where k < min{m, n}, so as to minimize ||A-D||_F^2. It is known that bysampling O(k/eps) rows of the matrix, one can find a low-rank approximation with additive error eps||A||_F^2. Our...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Language: | en_US |
| Published: |
2005
|
| Online Access: | http://hdl.handle.net/1721.1/30530 |