Iterative subspace analysis based on feature line distance

Yanwei Pang, Yuan Yuan, Xuelong Li

Research output: Contribution to journalArticlepeer-review


Nearest feature line-based subspace analysis is first proposed in this paper. Compared with conventional methods, the newly proposed one brings better generalization performance and incremental analysis. The projection point and feature line distance are expressed as a function of a subspace, which is obtained by minimizing the mean square feature line distance. Moreover, by adopting stochastic approximation rule to minimize the objective function in a gradient manner, the new method can be performed in an incremental mode, which makes it working well upon future data. Experimental results on the FERET face database and the UCI satellite image database demonstrate the effectiveness.
Original languageEnglish
Pages (from-to)903-907
Number of pages5
JournalIEEE Transactions on Image Processing
Issue number4
Publication statusPublished - Apr 2009


  • generalization performance
  • incremental analysis
  • iterative subspace analysis
  • mean square feature line distance
  • nearest feature line
  • objective function
  • projection point
  • stochastic approximation rule


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