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An Efficient Initialization Method for Nonnegative Matrix Factorization

An Efficient Initialization Method for Nonnegative Matrix Factorization. M. Rezaei, R. Boostani and M. Rezaei Journal of Applied Sciences, 11: 354-359,2011. Presenter Chia-Cheng Chen. Outline. Introduction Background review Results and discussion. Introduction.

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An Efficient Initialization Method for Nonnegative Matrix Factorization

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  1. An Efficient Initialization Method for Nonnegative Matrix Factorization M. Rezaei, R. Boostani and M. Rezaei Journal of Applied Sciences, 11: 354-359,2011 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. 

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