This paper develops a class of algorithms for general linear systems and eigenvalue
problems. These algorithms apply fast randomized dimension reduction (“sketching”) to …

This paper proposes a data collaboration analysis framework for distributed data sets. The
proposed framework involves centralized machine learning while the original data sets and …

This paper proposes an interpretable non-model sharing collaborative data analysis method
as a federated learning system, which is an emerging technology for analyzing distributed …

Feature selection is an efficient dimensionality reduction technique for artificial intelligence
and machine learning. Many feature selection methods learn the data structure to select the …

This paper proposes a novel non-model sharing-type collaborative learning method for
distributed data analysis, in which data are partitioned in both samples and features …

Under the privacy protection policy, federated learning has received more and more
attention. Vertical federated learning (VFL) uses the same samples local in different parties …

In this paper, based on the Riemannian optimization approach we propose a Riemannian
nonlinear conjugate gradient method with nonmonotone line search technique for solving …

Often the easiest way to discretize an ordinary or partial differential equation is by a
rectangular numerical method, in which n basis functions are sampled at m≫ n collocation …

In recent years, the accumulation of data across various institutions has garnered attention
for the technology of confidential data analysis, which improves analytical accuracy by …

A common way of finding the poles of a meromorphic function f in a domain, where an
explicit expression of f is unknown but f can be evaluated at any given z, is to interpolate f by …