Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification

Feature augmentation, which manipulates the feature space by integrating the label information, is one of the most popular strategies for solving Multi-Dimensional Classification (MDC) problems. However, the vanilla feature augmentation approaches fail to consider the intra-class exclusiveness, and may achieve degenerated performance. To fill this gap, a novel neural network based model is proposed which seamlessly integrates the Label Embedding and Feature Augmentation (LEFA) techniques to learn label correlations. Specifically, based on attentional factorization machine, a cross correlation aware network is introduced to learn a low-dimensional label representation that simultaneously depicts the inter-class correlations and the intra-class exclusiveness. Then the learned latent label vector can be used to augment the original feature space. Extensive experiments on seven real-world datasets demonstrate the superiority of LEFA over state-of-the-art MDC approaches.

Authors

• Haobo Wang Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
• Chen Chen University of Virginia
• Weiwei Liu Wuhan University
• Ke Chen The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
• Tianlei Hu Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
• Gang Chen

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