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Title: Enhancing bilinear subspace learning by element rearrangement
Authors: Xu, D.
Yan, S. 
Lin, S.
Huang, T.S.
Chang, S.-F.
Keywords: Bilinear subspace learning
Dimensionality reduction
Earth mover's distance
Element rearrangement
Issue Date: 2009
Citation: Xu, D., Yan, S., Lin, S., Huang, T.S., Chang, S.-F. (2009). Enhancing bilinear subspace learning by element rearrangement. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (10) : 1913-1920. ScholarBank@NUS Repository.
Abstract: 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. © 2009 IEEE.
Source Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN: 01628828
DOI: 10.1109/TPAMI.2009.51
Appears in Collections:Staff Publications

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