Principal component analysis (PCA) and subspace estimation (SE) are popular data
analysis tools and used in a wide range of applications. The main interest in PCA/SE is for …

In this article, we propose a novel algorithm, namely PETRELS-ADMM, to deal with
subspace tracking in the presence of outliers and missing data. The proposed approach …

Low-rank matrix completion is an important research topic with a wide range of applications.
One prevailing way for matrix recovery is based on rank minimization. Directly solving this …

The so-called block-term decomposition (BTD) tensor model, especially in its rank-version,
has been recently receiving increasing attention due to its enhanced ability of representing …

With the increased proliferation of smart devices, the transit passengers of today expect a
higher quality of service in the form of real-time traffic updates, accurate expected time-of …

This paper delves into the dynamic field of hyperspectral imaging, focusing on the
challenging task of denoising hyperspectral images given the presence of diverse noise …

Sampling of signals belonging to a low-dimensional subspace has well-documented merits
for dimensionality reduction, limited memory storage, and online processing of streaming …

In recent years, sparse subspace tracking has attracted increasing attention in the signal
processing community. In this paper, we propose a new provable effective method called …

We studied the problem of sparse subspace tracking in the high-dimensional regime where
the dimension is comparable to or much larger than the sample size. Leveraging power …

This paper investigates the problem of recovering missing samples using methods based on
sparse representation adapted for visually enhanced quality of reconstruction of image …