Kernel non-negative matrix factorization without the pre-image problem
Résumé
The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing. A great challenge arises when dealing with nonlinear NMF. In this paper, we propose an efficient nonlinear NMF, which is based on kernel machines. As opposed to previous work, the proposed method does not suffer from the pre-image problem. We propose two iterative algorithms: an additive and a multiplicative update rule. Several extensions of the kernel-NMF are developed in order to take into account auxiliary structural constraints, such as smoothness, sparseness and spatial regularization. The relevance of the presented techniques is demonstrated in unmixing a synthetic hyperspectral image.
Mots clés
bioinformatics
signal processing
blind source separation
hyperspectral image analysis
Polynomials
reproducing kernel Hilbert space
nonnegative matrix factorization
remote sensing
Estimation
nonlinear NMF
iterative algorithms
additive update rule
multiplicative update rule
auxiliary structural constraints
synthetic hyperspectral image unmixing
Kernel
Additives
Hyperspectral imaging
Vectors
kernel-NMF
kernel nonnegative matrix factorization
image processing
matrix decomposition
iterative methods
pre-image problem
unmixing problem
hyperspectral data
| Origine | Fichiers produits par l'(les) auteur(s) |
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