Learning Deep Latent Spaces for Multi-Label Classification

  title={Learning Deep Latent Spaces for Multi-Label Classification},
  author={Chih-Kuan Yeh and Wei-Chieh Wu and Wei-Jen Ko and Yu-Chiang Frank Wang},
Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the… CONTINUE READING
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CNN-RNN: A Unified Framework for Multi-label Image Classification

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2016
View 6 Excerpts
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Canonical Analysis of Several Sets of Variables

Jon Roberts Kettenring
View 4 Excerpts
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Largescale multi-label learning with missing labels

H.-F. Yu, P. Jain, P. Kar, I. S. Dhillon
ICML, 593–601. • 2014
View 7 Excerpts
Highly Influenced

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