Zobrazeno 1 - 10
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pro vyhledávání: '"Zhang, Xiongjun"'
In this paper, we study the problem of low-rank tensor learning, where only a few of training samples are observed and the underlying tensor has a low-rank structure. The existing methods are based on the sum of nuclear norms of unfolding matrices of
Externí odkaz:
http://arxiv.org/abs/2410.18402
In this paper, we study the problem of a batch of linearly correlated image alignment, where the observed images are deformed by some unknown domain transformations, and corrupted by additive Gaussian noise and sparse noise simultaneously. By stackin
Externí odkaz:
http://arxiv.org/abs/2212.05719
Autor:
Zhang, Xiongjun, Ng, Michael K.
Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer v
Externí odkaz:
http://arxiv.org/abs/2208.08287
One of the key problems in tensor completion is the number of uniformly random sample entries required for recovery guarantee. The main aim of this paper is to study $n_1 \times n_2 \times n_3$ third-order tensor completion based on transformed tenso
Externí odkaz:
http://arxiv.org/abs/2012.08784
Tensor data often suffer from missing value problem due to the complex high-dimensional structure while acquiring them. To complete the missing information, lots of Low-Rank Tensor Completion (LRTC) methods have been proposed, most of which depend on
Externí odkaz:
http://arxiv.org/abs/2012.00944
Autor:
Zhang, Xiongjun, Ng, Michael K.
In this paper, we study the sparse nonnegative tensor factorization and completion problem from partial and noisy observations for third-order tensors. Because of sparsity and nonnegativity, the underlying tensor is decomposed into the tensor-tensor
Externí odkaz:
http://arxiv.org/abs/2007.10626
Publikováno v:
In Machine Learning with Applications 15 September 2023 13
Publikováno v:
In Pattern Recognition September 2023 141
In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the opti
Externí odkaz:
http://arxiv.org/abs/1910.09979
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. The mai
Externí odkaz:
http://arxiv.org/abs/1907.01113