Journal of Systems Science and Complexity

, Volume 28, Issue 5, pp 1212–1230 | Cite as

On Eigen-matrix translation method for classification of biological data

  • Hao JiangEmail author
  • Yushan Qiu
  • Xiaoqing Cheng
  • Waiki Ching


Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines (SVMs) are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigenmatrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.


Classification dimension reduction eigen-matrix translation glycan data kernel method (KM) support vector machine (SVM) 


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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Hao Jiang
    • 1
    Email author
  • Yushan Qiu
    • 2
  • Xiaoqing Cheng
    • 2
  • Waiki Ching
    • 2
  1. 1.Department of Mathematics, School of InformationRenmin University of ChinaBeijingChina
  2. 2.Advanced Modeling and Applied Computing Laboratory, Department of MathematicsThe University of Hong KongHong KongChina

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