Neighborhood preserving ordinal regression

Yang Liu, Yan Liiu, Chun Chung Chan, Jing Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


Ordinal regression, which aims at determining the rating of a data item on a discrete rating scale, is an important research topic in pattern mining and multimedia data analysis. Most of the existing approaches of ordinal regression try to seek only one direction on which the projected data are well ranked. This setting largely limits the discriminative ability and may not describe the complicated distribution of the dataset very well. In this paper, we proposed a new algorithm called Neighborhood Preserving Ordinal Regression (NPOR), which aims to extract multiple projection directions from the original dataset according to the maximum margin and manifold preserving criteria. By optimizing the order information of the observations and preserving the intrinsic geometry of the dataset in a unified framework, NPOR provides faithful ordinal regression results to the new coming data samples. Experiments on various data sets demonstrate the effectiveness of the proposed algorithm.
Original languageEnglish
Title of host publicationICIMCS 2012 - Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Number of pages4
Publication statusPublished - 19 Nov 2012
Externally publishedYes
Event4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012 - Wuhan, China
Duration: 9 Sept 201211 Sept 2012


Conference4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012


  • Manifold learning
  • Maximum margin
  • Neighborhood Preserving Ordinal Regression

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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