Dimension reduction is one of the key data transformation techniques in machine learning
and knowledge discovery. It can be realized by using linear and nonlinear transformation …

The extreme learning machine (ELM) and the minimal learning machine (MLM) are
nonlinear and scalable machine learning techniques with a randomly generated basis. Both …

The Finnish publication channel quality ranking system was established in 2010. The
system is expert-based, where separate panels decide and update the rankings of a set of …

Reconstruction of past changes in monsoon climate from speleothem oxygen isotope (δ 18
O) records is complex because δ 18 O signals can be influenced by multiple factors …

Large-scale educational assessment studies (LSAs) regularly collect massive amounts of
very rich cognitive and contextual data of whole student populations. Currently, LSAs are …

Missing data are unavoidable in the real-world application of unsupervised machine
learning, and their nonoptimal processing may decrease the quality of data-driven models …

Abstract The Programme for International Student Assessment (PISA) is a worldwide study
that assesses the proficiencies of 15-year-old students in reading, mathematics, and science …

Big data related to manufacturing applications has the traits such as great quantity, multi-
sources, low value density, high complexity, and dynamic state. Traditional feature extraction …

The classical Taylor's formula is an elementary tool in mathematical analysis and function
approximation. Its role in the optimization theory, whose data-driven variants have a central …

Certain stereotypes can be associated with people from different countries. For example, the
Italians are expected to be emotional, the Germans functional, and the Chinese hard …