Maximal Correlation Embedding Network for Multilabel Learning with Missing Labels

Multilabel processing  

Multilabel learning, the problem of mapping each data instance to a subset of labels, appears frequently in many real-world applications. However, obtaining complete label annotation for every instance requires tremendous efforts, especially when the label set is large. As a result, multilabel learning with missing labels remains as a common challenge. Existing works either cannot handle missing labels or lack nonlinear expressiveness and scalability to large label set. In this paper, we present a novel end-to-end solution for multilabel learning with missing labels. Our algorithm, Maximal Correlation Embedding Network learns a low dimensional label embedding using an encoder-decoder architecture. It exploits label similarity through a maximal correlation regularization in the embedded label space to reduce the classification bias due to missing labels. A series of experiments on popular multilabel datasets demonstrate that our approach outperforms state of the art, both in complete data and partially observed data.


Conference Paper
Lu Li, Yang Li, Xiangxiang Xu, Shao-Lun Huang, and Lin Zhang, Maximal Correlation Embedding Network for Multilabel Learning with Missing Labels, 2019 IEEE International Conference on Multimedia and Expo (ICME), 2019 (Accepted) pdf ppt
Poster Abstract

We applied the maximal correlation embedding method to real-time human context recognition with missing labels.

Lu Li, Yang Li, Xiangxiang Xu, Lin Zhang, A Maximal Correlation Embedding Method for Multilabel Human Context Recognition, 18th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN’19), Montreal , Canada, April, 2019 (Accepted) pdf ppt

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