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Heterogeneous networks, consisting of multi-type objects coupled with various relations, are ubiquitous in the real world. Most previous work on clustering heterogeneous networks either converts them into homogeneous networks or simplifies the modeling of the heterogeneity in terms of specific objects, structures or assumptions. However, few studies consider all relevant objects and relations, and...
Distance metric learning is the task that aims to automate this process of learning task-specific distance functions in a supervised manner. In this paper, we study how to learn a Mahalanobis distance metric that can improve nearest neighbor classification. Our paper makes two contributions. First, we propose a novel framework named CLE_LMNN for Mahalanobis distance learning. CLE_LMNN builds on a...
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