Bo Li

Publications (2)0.7 Total impact

• Source

  Available from: posgrado.escom.ipn.mx

  Article: Feature extraction using maximum variance sparse mapping

  Jin Liu, Bo Li, Wen-Sheng Zhang
  [show abstract] [hide abstract]
  ABSTRACT: In this paper, a multiple sub-manifold learning method–oriented classification is presented via sparse representation, which is named maximum variance sparse mapping. Based on the assumption that data with the same label locate on a sub-manifold and different class data reside in the corresponding sub-manifolds, the proposed algorithm can construct an objective function which aims to project the original data into a subspace with maximum sub-manifold distance and minimum manifold locality. Moreover, instead of setting the weights between any two points directly or obtaining those by a square optimal problem, the optimal weights in this new algorithm can be approached using L1 minimization. The proposed algorithm is efficient, which can be validated by experiments on some benchmark databases. KeywordsMVSM–Sub-manifold–Sparse representation
  Neural Computing and Applications 04/2012; · 0.70 Impact Factor
• Conference Proceeding: The Connections between Principal Component Analysis and Dimensionality Reduction Methods of Manifolds.

  Bo Li, Jin Liu
  Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011, Revised Selected Papers; 01/2011