Class-imbalance learning based discriminant analysis

Xiaoyuan Jing, Chao Lan, Min Li, Yongfang Yao, Dapeng Zhang, Jingyu Yang

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

4 Citations (Scopus)


Feature extraction is an important research topic in the field of pattern recognition. The class-specific idea tends to recast a traditional multi-class feature extraction and recognition task into several binary class problems, and therefore inevitably class imbalance problem, where the minority class is the specific class, and the majority class consists of all the other classes. However, discriminative information from binary class problems is usually limited, and imbalanced data may have negative effect on the recognition performance. For solving these problems, in this paper, we propose two novel approaches to learn discriminant features from imbalanced data, named class-balanced discrimination (CBD) and orthogonal CBD (OCBD). For a specific class, we select a reduced counterpart class whose data are nearest to the data of specific class, and further divide them into smaller subsets, each of which has the same size as the specific class, to achieve balance. Then, each subset is combined with the minority class, and linear discriminant analysis (LDA) is performed on them to extract discriminative vectors. To further remove redundant information, we impose orthogonal constraint on the extracted discriminant vectors among correlated classes. Experimental results on three public image databases demonstrate that the proposed approaches outperform several related image feature extraction and recognition methods.
Original languageEnglish
Title of host publication1st Asian Conference on Pattern Recognition, ACPR 2011
Number of pages5
Publication statusPublished - 1 Dec 2011
Event1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China
Duration: 28 Nov 201128 Nov 2011


Conference1st Asian Conference on Pattern Recognition, ACPR 2011


  • class balanced discrimination (CBD)
  • class-imbalance learning
  • discriminant analysis
  • image feature extraction and recognition
  • orthogonal CBD (OCBD)

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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