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Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation
In this paper, we develop a new alternating projection method to compute nonnegative low rank matrix approximation for nonnegative matrices.
Nonnegative Low Rank Tensor Approximation and its Application to Multi-dimensional Images
The main aim of this paper is to develop a new algorithm for computing nonnegative low rank tensor approximation for nonnegative tensors that arise in many multi-dimensional imaging applications.
Robust Tensor Completion Using Transformed Tensor SVD
In this paper, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD.
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