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The curse of dimensionality is the main reason for the computational complexity and the Hughes phenomenon in supervised hyperspectral classification. Previous studies seldom consider in a simultaneous fashion the real situation of insufficiency of available training samples, particularly for small land covers that often contain the key information of the scene, and the problem of complexity. In this...
In many pattern recognition and data mining tasks, we often confront the problem of learning from a large amount of unlabeled data only with few pairwise constraints. This learning style is a kind of semi-supervised learning, and these pairwise constraints are called Side-Information. Generally speaking, these pairwise constraints are divided into two categories, one is called must-link if the pair...
Robustness is one of the most critical issues in the appearance-based learning strategies. In this work, we propose a novel kernel that is robust against data corruption for various visual learning problems. By incorporating a robust rho-function to relieve the influence of outliers, the proposed kernel is shown to be robust against various types of outliers. By incorporating the proposed kernel into...
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