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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...
Twin kernel embedding (TKE) is a powerful non-vectorial data reduction algorithm proposed for advanced applications in clustering and visualization, manifold learning, etc. Due to the requirement of online processing in many cutting edge research problems involving highly structured data like DNA, protein sequences and biometric features that are non-vectorial in nature, learning the out-of-sample...
Visualization of non-vectorial objects is not easy in practice due to their lack of convenient vectorial representation. Representative approaches are kernel PCA and kernel Laplacian eigenmaps introduced recently in our research. Extending our earlier work, we propose in this paper a new algorithm called twin kernel embedding (TKE) that preserves the similarity structure of input data in the latent...
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