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Twin kernel embedding (TKE) is a novel approach for visualization of non-vectorial objects. It preserves the similarity structure in high-dimensional or structured input data and reproduces it in a low dimensional latent space by matching the similarity relations represented by two kernel gram matrices, one kernel for the input data and the other for embedded data. However, there is no explicit mapping...
In this paper, a recently proposed dimensionality reduction method called twin kernel embedding (TKE) is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted...
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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