Unsupervised Deep Learning (DL) techniques have been widely used in various security-
related anomaly detection applications, owing to the great promise of being able to detect …

In this work, we consider a class of differentiable criteria for sparse image computing
problems, where a nonconvex regularization is applied to an arbitrary linear transform of the …

Sparse index tracking, as one of the passive investment strategies, is to track a benchmark
financial index via constructing a portfolio with a few assets in a market index. It can be …

Subspace clustering methods with sparsity prior, such as Sparse Subspace Clustering
(SSC) 1, are effective in partitioning the data that lie in a union of subspaces. Most of those …

The time-frequency (TF) analysis method plays a significant role in the detection of natural
gas hydrates. As a data-driven method, compressed sensing (CS) has been widely used in …

We review the field of multi-criteria optimization for radiation therapy treatment planning.
Special attention is given to the technique known as Pareto surface navigation, which allows …

In unsupervised domain adaptation, a key research problem is joint distribution alignment
across the source and target domains. However, direct alignment of the source and target …

In this study, a priority-aware lightweight secure sensing model for body area networks with
clinical healthcare applications in internet of things is proposed. In this model, patients' data …

Signal processing on graph offers the ability to define relationships of high-dimensional data
on graph. In this paper, an unsupervised feature extraction method using graph for …

We address the problem of finding a set of sparse signals that have nonzero coefficients in
the same locations from a set of their compressed measurements. A mixed lscr 2, 0 norm …