Trace Ratio Optimization With Feature Correlation Mining for Multiclass Discriminant Analysis

Authors

  • Forough Rezaei Boroujeni Griffith University, Gold Coast Campus
  • Sen Wang Griffith University, Gold Coast Campus
  • Zhihui Li Beijing Etrol Technologies Company Ltd.
  • Nicholas West Griffith University, Gold Coast Campus
  • Bela Stantic Griffith University, Gold Coast Campus
  • Lina Yao The University of New South Wales
  • Guodong Long University of Technology Sydney

Keywords:

Trace Ratio, L21 regularisation, Feature Extraction

Abstract

Fisher's linear discriminant analysis is a widely accepted dimensionality reduction method, which aims to find a transformation matrix to convert feature space to a smaller space by maximising the between-class scatter matrix while minimising the within-class scatter matrix. Although the fast and easy process of finding the transformation matrix has made this method attractive, overemphasizing the large class distances makes the criterion of this method suboptimal. In this case, the close class pairs tend to overlap in the subspace. Despite different weighting methods having been developed to overcome this problem, there is still a room to improve this issue. In this work, we study a weighted trace ratio by maximising the harmonic mean of the multiple objective reciprocals. To further improve the performance, we enforce the l2,1-norm to the developed objective function. Additionally, we propose an iterative algorithm to optimise this objective function. The proposed method avoids the domination problem of the largest objective, and guarantees that no objectives will be too small. This method can be more beneficial if the number of classes is large. The extensive experiments on different datasets show the effectiveness of our proposed method when compared with four state-of-the-art methods.

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Published

2018-04-29

How to Cite

Rezaei Boroujeni, F., Wang, S., Li, Z., West, N., Stantic, B., Yao, L., & Long, G. (2018). Trace Ratio Optimization With Feature Correlation Mining for Multiclass Discriminant Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11805