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Chapter and Conference Paper
Uncovering Locally Discriminative Structure for Feature Analysis
Manifold structure learning is often used to exploit geometric information among data in semi-supervised feature learning algorithms. In this paper, we find that local discriminative information is also of imp...
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Chapter and Conference Paper
Unsupervised Feature Analysis with Class Margin Optimization
Unsupervised feature selection has been attracting research attention in the communities of machine learning and data mining for decades. In this paper, we propose an unsupervised feature selection method seek...
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Chapter and Conference Paper
Modeling Relations and Their Mentions without Labeled Text
Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KB...