Name
Title | ||
|---|---|---|
Nonnegative matrix factorization with bounded total variational regularization for face recognition |
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of nonnegative data based on minimizing least square error (L"2 norm). However it has been observed that the proper norm for images is the bounded total variation (TV) norm other than the L"2 norm. The space of functions of bounded TV allows discontinuous solution and plays an important role in image processing. In this paper, we propose a new NMF model with bounded TV regularization for identifying discriminate representation of image patterns. We provide a simple update rule for computing the factorization and give supporting theoretical analysis. Finally, we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patrec.2010.08.001 | Pattern Recognition Letters |
Keywords | Field | DocType |
nonnegative matrix factorization,face recognition,numerical experiment,total variation regularization,image processing,image pattern,numerical scheme,proper norm,bounded tv regularization,bounded total variational regularization,bounded tv,bounded total variation,new nmf model,total variation | Applied mathematics,Image processing,Total variation denoising,Regularization (mathematics),Artificial intelligence,Mathematical optimization,Pattern recognition,Matrix decomposition,Non-negative matrix factorization,Factorization,Norm (mathematics),Mathematics,Bounded function | Journal |
Volume | Issue | ISSN |
31 | 16 | Pattern Recognition Letters |
Citations | PageRank | References |
6 | 0.58 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| HAIQING YIN | 1 | 26 | 3.67 |
| Hongwei Liu | 2 | 78 | 12.29 |