Manifold optimization is ubiquitous in computational and applied mathematics, statistics,
engineering, machine learning, physics, chemistry, etc. One of the main challenges usually …

Band selection is one of the most effective methods to reduce the band redundancy of
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …

Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a
robust variant of the Wasserstein distance. Recent work suggests that this quantity is more …

Near-term quantum computers will be limited in the number of qubits on which they can
process information as well as the depth of the circuits that they can coherently carry out. To …

Optimization on Riemannian manifolds widely arises in eigenvalue computation, density
functional theory, Bose--Einstein condensates, low rank nearest correlation, image …

To construct a parallel approach for solving optimization problems with orthogonality
constraints is usually regarded as an extremely difficult mission, due to the low scalability of …

From optimal transport to robust dimensionality reduction, many machine learning
applicationscan be cast into the min-max optimization problems over Riemannian manifolds …

In this paper, we focus on the decentralized optimization problem over the Stiefel manifold,
which is defined on a connected network of agents. The objective is an average of local …

Matrix factorization based multi-view clustering algorithms has attracted much attention in
recent years due to the strong interpretability and efficient implementation. In general, these …

Graph based methods have been widely used in incomplete multi-view clustering (IMVC).
Most recent methods try to fill the original missing samples or incomplete affinity matrices to …