AI RESEARCH
High-dimensional Semi-supervised Classification via the Fermat Distance
arXiv CS.LG
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ArXi:2604.23573v1 Announce Type: cross Semi-supervised classification, where unlabeled data are massive but labeled data are limited, often arises in machine learning applications. We address this challenge under high-dimensional data by leveraging the manifold and cluster assumptions. Based on the Fermat distance, a density-sensitive metric that naturally encodes the cluster assumption, we propose the weighted $k$-nearest neighbors (NN) classifier and multidimensional scaling (MDS)-induced classifiers.