We propose a sampling-based method for computing the tensor ring (TR) decomposition of
a data tensor. The method uses leverage score sampled alternating least squares to fit the …

Recent papers have developed alternating least squares (ALS) methods for CP and tensor
ring decomposition with a per-iteration cost which is sublinear in the number of input tensor …

Tensor decomposition has been proved to be effective for solving many problems in signal
processing and machine learning [1]. Recently, tensor decomposition finds its advantage for …

Randomized algorithms are efficient techniques for big data tensor analysis. In this tutorial
paper, we review and extend a variety of randomized algorithms for decomposing large …

Based on sketching techniques, we propose two practical randomized algorithms for tensor
ring (TR) decomposition. Specifically, on the basis of defining new tensor products and …

In learning tasks with high order correlations, the low-rank approximation of the regression
coefficient tensor has become increasingly important. Tensor ring can capture more …

The emergence of large-scale data demands new regression models with multi-dimensional
coefficient arrays, known as tensor regression models. The recently proposed tensor ring …

CNNs achieve high levels of performance by leveraging deep, over-parametrized neural
architectures, trained on large datasets. However, they exhibit limited generalization abilities …

Tensor train decomposition is a powerful tool for dealing with high-dimensional, large-scale
tensor data, which is not suffering from the curse of dimensionality. To accelerate the …

Tensor wheel (TW) decomposition combines the popular tensor ring and fully connected
tensor network decompositions and has achieved excellent performance in tensor …