Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Ensuring data quality in biomedical sciences is crucial for reliable research outcomes,
particularly as precision medicine continues to gain prominence. Missing values …

Recently, transform-based tensor nuclear norm (TNN) minimization methods have received
increasing attention for recovering third-order tensors in multi-dimensional imaging …

Low-rank quaternion tensor completion method, a novel approach to recovery color videos
and images, is proposed in this paper. We respectively reconstruct a color image and a color …

Missing data widely exist in industrial processes and lead to difficulties in modeling,
monitoring, fault diagnosis, and control. In this article, we propose a nonlinear method to …

Traditional recommendation methods based on matrix factorization techniques have yielded
immense success because of their good scalability. However, they still face the problem of …

Matrix factorization is a popular matrix completion method, however, it is difficult to
determine the ranks of the factor matrices. We propose two new sparse matrix factorization …

Recent advances in matrix completion enable data imputation in full-rank matrices by
exploiting low dimensional (nonlinear) latent structure. In this paper, we develop a new …

Recently, the tensor nuclear norm (TNN)-based tensor robust principle component analysis
(TRPCA) has achieved impressive performance in multidimensional data processing. The …

This paper proposes an effective method for accurately recovering vessel structures and
intensity information from the X-ray coronary angiography (XCA) images of moving organs …