1. An Efficient Initialization Method for Nonnegative Matrix Factorization
M. Rezaei, R. Boostani and M. Rezaei
Journal of Applied Sciences, 11: ,2011
Presenter Chia-Cheng Chen

2. Outline
Introduction
Background review
Results and discussion

3. Introduction
Although Non-negative Matrix Factorization has been employed in real applications but it still suffers from three shortcomings in terms of finding a suitable initialization method.
Enhance NMF performance using Fuzzy C-Means Clustering

4. Background review
Non-negative Matrix Factorization
Fuzzy C Means

5. Background review
The NMF method attempts to find a solution in order to decompose a given non-negative matrix A∈Rmxn into multiplication of two non-negative matrices w∈Rmxk and H∈Rkxn 

6. Background review Local Nonnegative Matrix Factorization (LNMF)
where, α, β>0 are constants and U = WTW and V = HHT

7. Background review
Fuzzy C Means

8. Background review Facial expression recognition Fixed geometry size
Normalized in the interval of 0 to 1

9. Results and discussion
JAFFE dataset is used containing 213 images include 7 facial expressions consisting 6 basic facial expressions and neutral expression that posed by 10 Japanese female models. 

10. Results and discussion

11. Results and discussion

12. Results and discussion
NMF is a part based representation that has been applied to many applicable such as dimension reduction, image segmentation, image compression and document clustering.