Deep learning models have achieved great success in solving a variety of natural language
processing (NLP) problems. An ever-growing body of research, however, illustrates the …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. A related easier problem is termed subspace learning or subspace estimation …

As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …

We consider a class of nonsmooth optimization problems over the Stiefel manifold, in which
the objective function is weakly convex in the ambient Euclidean space. Such problems are …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be …

We consider the problem of learning a linear subspace from data corrupted by outliers.
Classical approaches are typically designed for the case in which the subspace dimension …

Geometric model fitting is a fundamental task in computer vision, which serves as the pre-
requisite of many downstream applications. While the problem has a simple intrinsic …

The aim of this paper is twofold. First, we show that a certain concatenation of a proximity
operator with an affine operator is again a proximity operator on a suitable Hilbert space …

This paper revisits the problem of locating a signal-emitting source from time-difference-of-
arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently …