Initialization enhancer for non-negative matrix factorization
Engineering Applications of Artificial Intelligence
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
The Journal of Machine Learning Research
Expression recognition using fuzzy spatio-temporal modeling
Pattern Recognition
Fast nonnegative matrix factorization and its application for protein fold recognition
EURASIP Journal on Applied Signal Processing
Integrated Computer-Aided Engineering
Discriminant Non-negative Matrix Factorization and Projected Gradients for Frontal Face Verification
Biometrics and Identity Management
Discriminant nonnegative tensor factorization algorithms
IEEE Transactions on Neural Networks
Non-negative matrix factorization on Kernels
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Robust automatic data decomposition using a modified sparse NMF
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Nonlinear non-negative component analysis algorithms
IEEE Transactions on Image Processing
Boosted independent features for face expression recognition
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Human facial expression recognition using hybrid network of PCA and RBFN
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Two-dimensional non-negative matrix factorization for face representation and recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Fast non-negative dimensionality reduction for protein fold recognition
ECML'05 Proceedings of the 16th European conference on Machine Learning
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In this paper two image representation approaches called non-negative matrix factorization (NMF) and local non-negative matrix factorization (LNMF) have been applied to two facial databases for recognizing six basic facial expressions. A principal component analysis (PCA) approach was performed as well for facial expression recognition for comparison purposes. We found that, for the first database, LNMF outperforms both PCA and NMF, while NMF produces the poorest recognition performance. Results are approximately the same for the second database, with slightly performance improvement on behalf of NMF.