63 search results - page 7 / 13 » Learning low-rank kernel matrices |
NIPS
2004
13 years 9 months ago
2004
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the prob...
NIPS
2004
13 years 9 months ago
2004
The problem of learning a sparse conic combination of kernel functions or kernel matrices for classification or regression can be achieved via the regularization by a block 1-norm...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
A number of updates for density matrices have been developed recently that are motivated by relative entropy minimization problems. The updates involve a softmin calculation based...
MP
2011
13 years 2 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...