44 search results - page 8 / 9 » Spectral norm of random matrices |
NIPS
2004
13 years 11 months ago
2004
We present an algorithm based on convex optimization for constructing kernels for semi-supervised learning. The kernel matrices are derived from the spectral decomposition of grap...
SDM
2012
SIAM
12 years 5 days ago
2012
SIAM
Low-rank approximations which are computed from selected rows and columns of a given data matrix have attracted considerable attention lately. They have been proposed as an altern...
JAT
2010
13 years 8 months ago
2010
The paper investigates the properties of certain biorthogonal polynomials appearing in a specific simultaneous Hermite–Pad´e approximation scheme. Associated with any totally ...
MP
2011
13 years 4 months ago
2011
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This c...
STOC
2009
ACM
14 years 10 months ago
2009
ACM
The low-rank matrix approximation problem involves finding of a rank k version of a m ? n matrix AAA, labeled AAAk, such that AAAk is as "close" as possible to the best ...