Enhancing bilinear subspace learning by element rearrangement

Dong Xu, Shuicheng Yan, Stephen Lin, Thomas S. Huang, Shih Fu Chang

Research output: Contribution to journalArticlepeer-review


The success of bilinear subspace learning heavily depends on reducing correlations among features along rows and columns of the data matrices. In this work, we study the problem of rearranging elements within a matrix in order to maximize these correlations so that information redundancy in matrix data can be more extensively removed by existing bilinear subspace learning algorithms. An efficient iterative algorithm is proposed to tackle this essentially integer programming problem. In each step, the matrix structure is refined with a constrained Earth Mover's Distance procedure that incrementally rearranges matrices to become more similar to their low-rank approximations, which have high correlation among features along rows and columns. In addition, we present two extensions of the algorithm for conducting supervised bilinear subspace learning. Experiments in both unsupervised and supervised bilinear subspace learning demonstrate the effectiveness of our proposed algorithms in improving data compression performance and classification accuracy.

Original languageEnglish (US)
Pages (from-to)1913-1920
Number of pages8
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number10
StatePublished - 2009
Externally publishedYes


  • Bilinear subspace learning
  • Dimensionality reduction
  • Earth mover's distance
  • Element rearrangement

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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