Online nonnegative matrix factorization based on kernel machines
Résumé
Nonnegative matrix factorization (NMF) has been increasingly investigated for data analysis and dimension-reduction. To tackle large-scale data, several online techniques for NMF have been introduced recently. So far, the online NMF has been limited to the linear model. This paper develops an online version of the nonlinear kernel-based NMF, where the decomposition is performed in the feature space. Taking the advantage of the stochastic gradient descent and the mini-batch scheme, the proposed method has a fixed, tractable complexity independent of the increasing samples number. We derive the multiplicative update rules of the general form, and describe in detail the case of the Gaussian kernel. The effectiveness of the proposed method is validated on unmixing hyperspectral images, compared with the state-of-the-art online NMF methods.
Mots clés
data analysis
Gaussian processes
gradient methods
geophysical image processing
hyperspectral imaging
matrix decomposition
Signal processing
Europe
Linear programming
Encoding
Kernel
unmixing hyperspectral imaging
online learning
kernel machines
Nonnegative matrix factorization
Computational complexity
Stochastic processes
online nonnegative matrix factorization
kernel machine
dimension-reduction
nonlinear kernel-based NMF
stochastic gradient descent scheme
minibatch scheme
Gaussian kernel
hyperspectral unmixing
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