Projected gradient methods for linearly constrained problems
Mathematical Programming: Series A and B
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of distance metrics for recognition based on non-negative matrix factorization
Pattern Recognition Letters
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
Kernel independent component analysis
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
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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
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Nonnegative matrix factorization with quadratic programming
Neurocomputing
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Non-negative matrix factorization on Kernels
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
IEEE Transactions on Information Forensics and Security - Part 2
On the convergence of the block nonlinear Gauss-Seidel method under convex constraints
Operations Research Letters
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Pattern Recognition Letters
Multiple kernel local Fisher discriminant analysis for face recognition
Signal Processing
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Nonnegative matrix factorization (NMF) is a technique for analyzing the data structure when nonnegative constraints are imposed. However, NMF aims at minimizing the objective function from the viewpoint of data reconstruction and thus it may produce undesirable performances in classification tasks. In this paper, we develop a novel NMF algorithm (called KDNMF) by optimizing the objective function in a feature space under nonnegative constraints and discriminant constraints. The KDNMF method exploits the geometrical structure of data points and seeks the tradeoff between data reconstruction errors and the geometrical structure of data. The projected gradient method is used to solve KDNMF since directly using the multiplicative update algorithm to update nonnegative matrices is impractical for Gaussian kernels. Experiments on facial expression images and face images are conducted to show the effectiveness of the proposed method.