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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...
Dimensionality reduction (DR) has been considered as one of the most significant tools for data analysis. One type of DR algorithms is based on latent variable models (LVM). LVM-based models can handle the preimage problem easily. In this paper we propose a new LVM-based DR model, named thin plate spline latent variable model (TPSLVM). Compared to the well-known Gaussian process latent variable model...
Ground Penetrating Radar (GPR) offers the ability to observe the internal structure of a pile of rocks. Large fragments within the pile may not be visible on the surface. Determining these large fragment sizes before collection can improve mine productivity. This research has examined the potential to identify objects where the background media and the object exhibit the same dielectric properties...
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