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
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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
