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Element Rearrangement for Tensor-Based Subspace Learning

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Element Rearrangement for Tensor-Based Subspace Learning
The success of tensor-based subspace learning depends heavily on reducing correlations along the column vectors of the mode-k flattened matrix. In this work, we study the problem of rearranging elements within a tensor in order to maximize these correlations, so that information redundancy in tensor data can be more extensively removed by existing tensor-based dimensionality reduction algorithms. An efficient iterative algorithm is proposed to tackle this essentially integer optimization problem. In each step, the tensor structure is refined with a spatially-constrained Earth Mover's Distance procedure that incrementally rearranges tensors to become more similar to their low rank approximations, which have high correlation among features along certain tensor dimensions. Monotonic convergence of the algorithm is proven using an auxiliary function analogous to that used for proving convergence of the ExpectationMaximization algorithm. In addition, we present an extension of the alg...
Shuicheng Yan, Dong Xu, Stephen Lin, Thomas S. Hua
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Shuicheng Yan, Dong Xu, Stephen Lin, Thomas S. Huang, Shih-Fu Chang
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