Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Large datasets are increasingly common and are often difficult to interpret. Principal
component analysis (PCA) is a technique for reducing the dimensionality of such datasets …

This paper proposes scalable and fast algorithms for solving the Robust PCA problem,
namely recovering a low-rank matrix with an unknown fraction of its entries being arbitrarily …

Unlike the conventional facial expressions, micro-expressions are involuntary and transient
facial expressions capable of revealing the genuine emotions that people attempt to hide …

As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …

As a convex relaxation of the low rank matrix factorization problem, the nuclear norm
minimization has been attracting significant research interest in recent years. The standard …

Many machine learning and signal processing problems can be formulated as linearly
constrained convex programs, which could be efficiently solved by the alternating direction …

The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …

Noise causes unpleasant visual effects in low-light image/video enhancement. In this paper,
we aim to make the enhancement model and method aware of noise in the whole process …

Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …