Ranking with uncertain labels and its applications

Shuicheng Yan, Huan Wang, Jianzhuang Liu, Xiao'Ou Tang, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


The techniques for image analysis and classification generally consider the image sample labels fixed and without uncertainties. The rank regression problem studied in this paper is based on the training samples with uncertain labels, which often is the case for the manual estimated image labels. A core ranking model is designed first as the bilinear fusing of multiple candidate kernels. Then, the parameters for feature selection and kernel selection are learned simultaneously by maximum a posteriori for given samples and uncertain labels. The provable convergency Expectation Maximization (EM) method is used for inferring these parameters in an iterative manner. The effectiveness of the proposed algorithm is finally validated by the extensive experiments on age ranking task and human tracking task. The popular FG-NET and the large scale Yamaha aging database are used for the age estimation experiments, and our algorithm outperforms those state-of-the-art algorithms ever reported by other interrelated literatures significantly. The experiment result of human tracking task also validates its advantage over conventional linear regression algorithm.

Original languageEnglish (US)
Pages (from-to)407-412
Number of pages6
JournalFrontiers of Computer Science in China
Issue number4
StatePublished - Oct 2007
Externally publishedYes


  • Age estimation
  • Human tracking
  • Uncertain label

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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