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This paper proposes a novel method for cross-modal retrieval. In addition to the traditional vector (text)-to-vector (image) framework, we adopt a matrix (text)-to-matrix (image) framework to faithfully characterize the structures of different feature spaces. Moreover, we propose a novel metric learning framework to learn a discriminative structured subspace, in which the underlying data distribution...
In multi-label classification, labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the global label correlations. However, the label correlations may be different over different data groups. In this paper, we propose a simple and efficient framework for multi-label classification,...
On social media, the user generated contents, e.g., Articles and images, can be assigned with multiple labels. In this paper, we focus on the problem of performing multi-label classification on social media data, where the user generated contents are associated with multiple labels. Multi-label learning studies the problem where each object is represented by a single instance and associated with a...
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