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Multilinear Jointly Sparse Robust Discriminant Regression

Published: 11 January 2021 Publication History

Abstract

Tensor data, such as image, video, etc. is drawing more and more attention from researchers. Therefore, in this paper, we will focus on the tensor data, proposing a novel tensor-based feature extraction model. Previously, Lai et al. proposed Robust Discriminant Regression (RDR) by using L2, 1 -norm as basic metric to improve the robustness of model. However, since RDR is based on vectors, it requires high computation cost for optimization. In this paper, we propose an improved method called Multilinear Jointly Sparse RDR (MJSRDR), whose inputs are tensors. Therefore, the computation complexity of algorithm can be reduced. Besides, to obtain jointly sparse projections, we introduce L2, 1 -norm as regularization term. Experiments on various datasets verify the effectiveness of the model.

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      ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition
      October 2020
      552 pages
      ISBN:9781450387835
      DOI:10.1145/3436369
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      Published: 11 January 2021

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      1. Tensor data
      2. jointly sparse
      3. robust discriminant regression

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