Abstract:Effectively measuring similarity among data samples represented as points in high-dimensional spaces remains a major challenge in retrieval, machine learning, and computer vision. In these scenarios, unsupervised manifold learning techniques grounded on rank information have been demonstrated to be a promising solution. However, various methods rely on rank correlation measures, which often depend on a proper definition of neighborhood size. On current approaches, this definition may lead to a reduction in the… Show more
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