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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...
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...
For the dermoscopy image, uneven illumination will influence segmentation accuracy and lead to wrong aided diagnosis result. In this paper, an uneven illumination removal method based on deep learning is proposed for dermoscopy images. Different from the traditional Retinex based methods, which estimate the illumination component using statistical methods to obtain the reflectance component of the...
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