Correlation embedding analysis

Yun Fu, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To design geometrically motivated approaches for classifying the high-dimensional data, we propose to learn a discriminant subspace using Correlation Embedding Analysis (CEA). This novel algorithm enhances its discriminant power by incorporating both correlational graph embedding and Fisher criterion. In a geometric interpretation, it projects the high-dimensional data onto a hypersphere and preserves intrinsic neighbor relations with the Pearson correlation metric. After the embedding, resulting data pairs from the same class are forced to enhance their correlation affinity, whereas neighboring points of different class are forced to reduce their correlation affinity at the same time. The feature learned by CEA is tolerable to scaling or outlier. Experiments on face recognition demonstrate the effectiveness and advantage of the CEA.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages1696-1699
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
CountryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

Keywords

  • Correlation embedding analysis
  • Discriminant analysis
  • Graph embedding
  • Subspace learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Fu, Y., & Huang, T. S. (2008). Correlation embedding analysis. In 2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings (pp. 1696-1699). [4712100] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2008.4712100