145 search results - page 3 / 29 » On Constrained Sparse Matrix Factorization |
JMLR
2010
13 years 3 months ago
2010
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
CORR
2004
Springer
13 years 5 months ago
2004
Springer
Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been a...
ICASSP
2011
IEEE
12 years 9 months ago
2011
IEEE
While nonnegative matrix factorization (NMF) has successfully been applied for gain-robust multi-pitch detection, a method to track pitch values over time was not provided. We emb...
JMLR
2006
2006
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
13 years 5 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...
CORR
2008
Springer
13 years 5 months ago
2008
Springer
We present a convex formulation of dictionary learning for sparse signal decomposition. Convexity is obtained by replacing the usual explicit upper bound on the dictionary size by...