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 …

Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR
Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation. It …

Robust principal component analysis (RPCA) is a critical tool in modern machine learning,
which detects outliers in the task of low-rank matrix reconstruction. In this paper, we propose …

While uniform sampling has been widely studied in the matrix completion literature, CUR
sampling approximates a low-rank matrix via row and column samples. Unfortunately, both …

The reconstruction of quantum states from experimental measurements, often achieved
using quantum state tomography (QST), is crucial for the verification and benchmarking of …

Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node
representations from data features. However, existing LGI methods commonly suffer from the …

Tensor train decomposition is widely used in machine learning and quantum physics due to
its concise representation of high-dimensional tensors, overcoming the curse of …

Low rank tensor approximation is a fundamental tool in modern machine learning and data
science. In this paper, we study the characterization, perturbation analysis, and an efficient …

We study the tensor robust principal component analysis (TRPCA) problem, a tensorial
extension of matrix robust principal component analysis, which aims to split the given tensor …

We study the problem of tensor robust principal component analysis (TRPCA), that aims to
separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their …