Principal component analysis is a versatile statistical method for reducing a cases-by-
variables data table to its essential features, called principal components. Principal …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

This review presents a modern perspective on dynamical systems in the context of current
goals and open challenges. In particular, our review focuses on the key challenges of …

In many applications, it is important to reconstruct a fluid flow field, or some other high-
dimensional state, from limited measurements and limited data. In this work, we propose a …

Randomized numerical linear algebra-RandNLA, for short-concerns the use of
randomization as a resource to develop improved algorithms for large-scale linear algebra …

In this article, we study the recursive algorithms for a class of separable nonlinear models
(SNLMs) in which the parameters can be partitioned into a linear part and a nonlinear part …

In most conditions, it is a problematic mission for a machine-learning model with a data
record, which has various attributes, to be trained. There is always a proportional …

We use a fully data-driven approach and information provided by the IMF's financial
soundness indicators to measure the condition of a country's financial system around the …

Shallow cloud fields over the subtropical ocean exhibit many spatial patterns. The frequency
of occurrence of these patterns can change under global warming. Hence, they may …

Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the
emergence of 'big data'has severely challenged our ability to compute this fundamental …