SHI Jiarong, JIAO Licheng, SHANG Fanhua, “Metric Learning for High-Dimensional Tensor Data,” Chinese Journal of Electronics, vol. 20, no. 3, pp. 495-498, 2011,
Citation: SHI Jiarong, JIAO Licheng, SHANG Fanhua, “Metric Learning for High-Dimensional Tensor Data,” Chinese Journal of Electronics, vol. 20, no. 3, pp. 495-498, 2011,

Metric Learning for High-Dimensional Tensor Data

  • Received Date: 2009-12-01
  • Rev Recd Date: 2011-02-01
  • Publish Date: 2011-07-25
  • This paper investigates how to learn the distance between multilinear samples. First, for tensor data, we present a new distance metric called as tensorbased Mahalanobis distance. Then the distance is learned through solving a model of tensor-based maximally collapsing metric learning. The proposed metric learning technique has the advantage of few parameters. At the same time, it is also employed to perform dimensionality reduction. Finally, face recognition experiments demonstrate the superiority of the learned distance over the Euclidean distance.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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