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Bi-directional Representation Learning for Multi-label Classification
[chapter]
2014
Lecture Notes in Computer Science
Multi-label classification is a central problem in many application domains. In this paper, we present a novel supervised bi-directional model that learns a low-dimensional mid-level representation for multilabel classification. Unlike traditional multi-label learning methods which identify intermediate representations from either the input space or the output space but not both, the mid-level representation in our model has two complementary parts that capture intrinsic information of the
doi:10.1007/978-3-662-44851-9_14
fatcat:q4dq2dkgdfdmjil2s2gwbbbqsm