Name
Abstract | ||
|---|---|---|
Non-negative matrix factorization (NMF) is a power- ful feature extraction method for finding parts-based, linear representations of non-negative data . Inherently, it is unsupervised learning algorithm. That is to say, the classical NMF algorithm does not respect the class-specific information. This paper presents an im- provement of the classical NMF approach by imposing Fisher constraints. This results in a two-step factoriza- tion procedure for discriminative feature extraction. Furthermore, weighting factors for each pairwise scatter is introduced to include the confusability information into the between class covariance matrix. The proposed method has been applied to the problem of face and handwritten digit recognition and the experiments give better performance than previous methods. |
Year | Venue | Keywords |
|---|---|---|
2007 | MVA | non negative matrix factorization,covariance matrix,unsupervised learning,feature extraction |
Field | DocType | Citations |
Pairwise comparison,Weighting,Pattern recognition,Matrix decomposition,Feature extraction,Non-negative matrix factorization,Artificial intelligence,Factorization,Digit recognition,Covariance matrix,Mathematics | Conference | 1 |
PageRank | References | Authors |
0.35 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Li | 1 | 22 | 12.44 |
| Kazuhiro Fukui | 2 | 828 | 71.55 |