Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Journal of Cognitive Neuroscience
Incremental subspace learning via non-negative matrix factorization
Pattern Recognition
Non-negative matrix factorization: Ill-posedness and a geometric algorithm
Pattern Recognition
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using underapproximations for sparse nonnegative matrix factorization
Pattern Recognition
Nonlinear non-negative component analysis algorithms
IEEE Transactions on Image Processing
Topology Preserving Non-negative Matrix Factorization for Face Recognition
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Nonlinear blind source separation using kernels
IEEE Transactions on Neural Networks
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Generalizations ofnonnegative matrix factorization (NMF) in kernel feature space, such as projected gradient kernel NMF (PGKNMF) and polynomial Kernel NMF (PNMF), have been developed for face and facial expression recognition recently. However, these existing kernel NMF approaches cannot guarantee the nonnegativity of bases in kernel feature space and thus are essentially semi-NMF methods. In this paper, we show that nonlinear semi-NMF cannot extract the localized components which offer important information in object recognition. Therefore, nonlinear NMF rather than semi-NMF is needed to be developed for extracting localized component as well as learning the nonlinear structure. In order to address the nonlinear problem of NMF and the semi-nonnegative problem of the existing kernel NMF methods, we develop the nonlinear NMF based on a self-constructed Mercer kernel which preserves the nonnegative constraints on both bases and coefficients in kernel feature space. Experimental results in face and expressing recognition show that the proposed approach outperforms the existing state-of-the-art kernel methods, such as KPCA, GDA, PNMF and PGKNMF.