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Found 6 papers, 2 papers with code
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction
Dimensionality reduction is an essential technique for multi-way large-scale data, i. e., tensor.
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection.
Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion
In low-rank tensor completion tasks, due to the underlying multiple large-scale singular value decomposition (SVD) operations and rank selection problem of the traditional methods, they suffer from high computational cost and high sensitivity of model complexity.
High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Format
We propose two TT-based algorithms: Tensor Train Weighted Optimization (TT-WOPT) and Tensor Train Stochastic Gradient Descent (TT-SGD) to optimize TT decomposition factors.
High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization
In this paper, we aim at the problem of tensor data completion.
Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition
In this paper, we aim at the completion problem of high order tensor data with missing entries.
