7 search results - page 1 / 2 » Learning Sparse Representations by Non-Negative Matrix Facto... |
JMLR
2006
2006
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
13 years 4 months ago
We exploit the biconvex nature of the Euclidean non-negative matrix factorization (NMF) optimization problem to derive optimization schemes based on sequential quadratic and secon...
ICCV
2005
IEEE
14 years 6 months ago
2005
IEEE
Example-based learning of codes that statistically encode general image classes is of vital importance for computational vision. Recently, non-negative matrix factorization (NMF) ...
ECCV
2006
Springer
14 years 6 months ago
2006
Springer
Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient image representation (Welling and Weber, Patt. Rec. Let., 2001). Until now, sparsity of t...
CORR
2002
Springer
13 years 4 months ago
2002
Springer
Abstract. Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this...
ICIP
2008
IEEE
14 years 6 months ago
2008
IEEE
In this paper, we address the problem of hallucinating a high resolution face given a low resolution input face. The problem is approached through sparse coding. To exploit the fa...