GMNS-based tensor decomposition

In this thesis, we focus on the divide-and-conquer approach for PARAFAC and highorder singular value decomposition (HOSVD) of three-way tensors. HOSVD is a specific orthogonal form of Tucker decomposition. Recently, generalized minimum noise subspace (GMNS) was proposed by Nguyen et al. in as a good...

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主要作者: Lê, Trung Thành
其他作者: Nguyễn, Linh Trung
格式: Theses
語言:English
出版: 2020
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在線閱讀:http://repository.vnu.edu.vn/handle/VNU_123/69892
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總結:In this thesis, we focus on the divide-and-conquer approach for PARAFAC and highorder singular value decomposition (HOSVD) of three-way tensors. HOSVD is a specific orthogonal form of Tucker decomposition. Recently, generalized minimum noise subspace (GMNS) was proposed by Nguyen et al. in as a good technique for subspace analysis. This method is highly beneficial in practice because it not only substantially reduces the computational complexity in finding bases for these subspaces, but also provides high estimation accuracy. This motivates us to propose in this thesis new implementations for tensor decomposition based on GMNS