Issue No. 10 - October (2009 vol. 31)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.51
Dong Xu , Nanyang Technological University, Singapore
Shuicheng Yan , National University of Singapore, Singapore
Stephen Lin , Microsoft Research Asia, Beijing
Thomas S. Huang , University of Illinois at Urbana-Champaign, Urbana
Shih-Fu Chang , Columbia University, New York
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.
Bilinear subspace learning, element rearrangement, earth mover's distance, dimensionality reduction.
S. Lin, D. Xu, T. S. Huang, S. Yan and S. Chang, "Enhancing Bilinear Subspace Learning by Element Rearrangement," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 1913-1920, 2009.