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Discriminative features are significant for hyper-spectral image (HSI) classification. In this letter, we apply the supervised dimensionality reduction (DR) model termed supervised latent linear Gaussian process latent variable model (SLLGPLVM) for feature extraction. As a semiparametric classification model, the new model has ability in simultaneous feature extraction and classification and demonstrates...
Unsupervised learning aims to discovery latent representation embedded in the observation, which is useful for data visualization, dimensionality reduction, and density modeling. Autoencoders have been successfully used to learn the latent variations in data, especially with the recent reintroduction by deep learning. For some specific tasks, there are supervised information or labels that can be...
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