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Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images
The most common way to introduce spatial information is to rely on a spatial regularization of the abundance maps.
Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis
The attribution vectors of the clustering are then used as features vectors for the classification task, i. e., the coding vectors of the corresponding factorization problem.
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
Within a supervised classification framework, labeled data are used to learn classifier parameters.
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