Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

Tensor recovery is a fundamental problem in tensor research field. It generally requires to
explore intrinsic prior structures underlying tensor data, and formulate them as certain forms …

Five-dimensional seismic reconstruction is receiving increasing attention and can be viewed
as a tensor completion problem, which involves reconstructing a low-rank tensor from a …

As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition
shows superior performance compared to other tensor decomposition formats. Existing TT …

In signal processing and data analytics, Wiener filter is a classical powerful tool to transform
an input signal to match a desired or target signal by a linear time-invariant (LTI) filter. The …

The approach of least squares (LSs) has been quite popular and widely adopted for the
common linear regression analysis, which can give rise to the solution to an arbitrary …

We propose a new accurate regularized Tucker decomposition (ARTD) method for image
restoration (IR), which considers global low-rankness and local similarity of intrinsic image …

The tensor train (TT) format enjoys appealing advantages in handling structural high-order
tensors. The recent decade has witnessed the wide applications of TT-format tensors from …

Tensor completion methods based on the tensor train (TT) have the issues of inaccurate
weight assignment and ineffective tensor augmentation pre-processing. In this work, we …