Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning the parts of objects by auto-association
Neural Networks
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
Learning sparse features for classification by mixture models
Pattern Recognition Letters
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
Representing image matrices: eigenimages versus eigenvectors
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
A "nonnegative PCA" algorithm for independent component analysis
IEEE Transactions on Neural Networks
SVD based initialization: A head start for nonnegative matrix factorization
Pattern Recognition
Face Image Recognition Combining Holistic and Local Features
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
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Non-negative matrix factorization (NMF) is a recently developed method for finding parts-based representation of non-negative data such as face images. Although it has successfully been applied in several applications, directly using NMF for face recognition often leads to low performance. Moreover, when performing on large databases, NMF needs considerable computational costs. In this paper, we propose a novel NMF method, namely 2DNMF, which stands for 2-D non-negative matrix factorization. The main difference between NMF and 2DNMF is that the former first align images into 1D vectors and then represents them with a set of 1D bases, while the latter regards images as 2D matrices and represents them with a set of 2D bases. Experimental results on several face databases show that 2DNMF has better image reconstruction quality than NMF under the same compression ratio. Also the running time of 2DNMF is less, and the recognition accuracy higher than that of NMF.