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In this paper we propose a method for visualizing unlabeled high dimensional data in a 3-dimensional space using Kernel Principal Component Analysis (KPCA) with the proposed unsupervised deep kernel. First, an optimal cluster structure of the data is determined using an unsupervised procedure. Second, an unsupervised deep kernel is learned via the clustered data. Then, deep kernel based PCA is applied...
This paper studies feasibility and scalable computing processes for visualizing big high dimensional data in a 3 dimensional space by using dimension reduction techniques. More specifically, we propose an unsupervised approach to compute a measure that is called visualizability in a 3 dimensional space for a high dimensional data. This measure of visualizability is computed based on the comparison...
Kernel function implicitly maps data from its original space to a higher dimensional feature space. Kernel based machine learning algorithms are typically applied to data that is not linearly separable in its original space. Although kernel methods are among the most elegant part of machine learning, it is challenging for users to define or select a proper kernel function with optimized parameter...
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