Metric-Based Auto-Instructor for Learning Mixed Data Representation

Authors

  • Songlei Jian National University of Defense Technology, University of Technology Sydney
  • Liang Hu University of Technology Sydney
  • Longbing Cao University of Technology Sydney
  • Kai Lu National University of Defense Technology

Keywords:

representation learning, mixed data, metric learning

Abstract

Mixed data with both categorical and continuous features are ubiquitous in real-world applications. Learning a good representation of mixed data is critical yet challenging for further learning tasks. Existing methods for representing mixed data often overlook the heterogeneous coupling relationships between categorical and continuous features as well as the discrimination between objects. To address these issues, we propose an auto-instructive representation learning scheme to enable margin-enhanced distance metric learning for a discrimination-enhanced representation. Accordingly, we design a metric-based auto-instructor (MAI) model which consists of two collaborative instructors. Each instructor captures the feature-level couplings in mixed data with fully connected networks, and guides the infinite-margin metric learning for the peer instructor with a contrastive order. By feeding the learned representation into both partition-based and density-based clustering methods, our experiments on eight UCI datasets show highly significant learning performance improvement and much more distinguishable visualization outcomes over the baseline methods.

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Published

2018-04-29

How to Cite

Jian, S., Hu, L., Cao, L., & Lu, K. (2018). Metric-Based Auto-Instructor for Learning Mixed Data Representation. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11597