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Found 7 papers, 0 papers with code
Tensor Factorization via Transformed Tensor-Tensor Product for Image Alignment
The main advantage of transformed tensor-tensor product is that its computational complexity is lower compared with the existing literature based on transformed tensor nuclear norm.
Sparse Nonnegative Tucker Decomposition and Completion under Noisy Observations
In this paper, we propose a sparse nonnegative Tucker decomposition and completion method for the recovery of underlying nonnegative data under noisy observations.
Tensor Completion by Multi-Rank via Unitary Transformation
The main aim of this paper is to study $n_1 \times n_2 \times n_3$ third-order tensor completion based on transformed tensor singular value decomposition, and provide a bound on the number of required sample entries.
Tensor Completion via Convolutional Sparse Coding Regularization
On the contrary, in the field of signal processing, Convolutional Sparse Coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data.
Sparse Nonnegative Tensor Factorization and Completion with Noisy Observations
We propose to minimize the sum of the maximum likelihood estimation for the observations with nonnegativity constraints and the tensor $\ell_0$ norm for the sparse factor.
Orthogonal Nonnegative Tucker Decomposition
In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD).
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.
