Label Enhancement with Sample Correlations via Low-Rank Representation

Authors

  • Haoyu Tang School of Software Engineering of Xi'an Jiaotong University
  • Jihua Zhu School of Software Engineering of Xi'an Jiaotong University
  • Qinghai Zheng Xi'an Jiaotong University
  • Jun Wang Shanghai Institute for Advanced Communication and Data Science
  • Shanmin Pang Xi'an Jiaotong University
  • Zhongyu Li Xi'an Jiaotong University

DOI:

https://doi.org/10.1609/aaai.v34i04.6053

Abstract

Compared with single-label and multi-label annotations, label distribution describes the instance by multiple labels with different intensities and accommodates to more-general conditions. Nevertheless, label distribution learning is unavailable in many real-world applications because most existing datasets merely provide logical labels. To handle this problem, a novel label enhancement method, Label Enhancement with Sample Correlations via low-rank representation, is proposed in this paper. Unlike most existing methods, a low-rank representation method is employed so as to capture the global relationships of samples and predict implicit label correlation to achieve label enhancement. Extensive experiments on 14 datasets demonstrate that the algorithm accomplishes state-of-the-art results as compared to previous label enhancement baselines.

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Published

2020-04-03

How to Cite

Tang, H., Zhu, J., Zheng, Q., Wang, J., Pang, S., & Li, Z. (2020). Label Enhancement with Sample Correlations via Low-Rank Representation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5932-5939. https://doi.org/10.1609/aaai.v34i04.6053

Issue

Section

AAAI Technical Track: Machine Learning