12 search results - page 1 / 3 » Spectral Regularization Algorithms for Learning Large Incomp... |
JMLR
2010
12 years 11 months ago
2010
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
CIKM
2007
Springer
13 years 11 months ago
2007
Springer
We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from...
ICCV
2007
IEEE
14 years 6 months ago
2007
IEEE
Subspace learning based face recognition methods have attracted considerable interests in recent years, including Principal Component Analysis (PCA), Linear Discriminant Analysis ...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We propose a general and efficient algorithm for learning low-rank matrices. The proposed algorithm converges super-linearly and can keep the matrix to be learned in a compact fac...
NIPS
2007
13 years 6 months ago
2007
Learning the common structure shared by a set of supervised tasks is an important practical and theoretical problem. Knowledge of this structure may lead to better generalization ...