Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Component-Based Face Recognition with 3D Morphable Models
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Face recognition using localized features based on non-negative sparse coding
Machine Vision and Applications
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data compression and harmonic analysis
IEEE Transactions on Information Theory
Bayesian Tensor Approach for 3-D Face Modeling
IEEE Transactions on Circuits and Systems for Video Technology
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks
A novel iris segmentation using radial-suppression edge detection
Signal Processing
Outlier-resisting graph embedding
Neurocomputing
Nonnegative matrix factorization with bounded total variational regularization for face recognition
Pattern Recognition Letters
ACM SIGGRAPH Asia 2010 papers
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
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The low-rank approximation technique of nonnegative matrix factorization (NMF) is emerging recently for finding parts-based structure of nonnegative data based on minimizing least-square error (L"2 norm). However, it has been observed that the proper norm for image processing is the total variation norm (TVN) other than the L"2 norm, and image denoising methods applying TVN can preserve clearer local features, such as edges and texture than L"2 norm. In this paper, we propose a robust TVN-based NMF algorithm for identifying discriminant representation of image patterns. We provide update rule in optimality search process and prove mathematically convergence of the iteration. Experimental results show that the proposed TVNMF is more effective to describe local discriminant representation of image patterns than NMF.