Sustainable development is an integrated approach to tackle ongoing global challenges
such as resource depletion, environmental degradation, and climate change. However, a …

Feature selection is an important approach for reducing the dimension of high-dimensional
data. In recent years, many feature selection algorithms have been proposed, but most of …

Motivation The development of microscopic imaging techniques enables us to study protein
subcellular locations from the tissue level down to the cell level, contributing to the rapid …

Isomap aims to recover the intrinsic geometric structure of manifold by preserving geodesic
distances between all pairs of data points. However it is an unsupervised dimensionality …

Common subspace learning methods only utilize local or global structure in feature
extraction, and cannot obtain the global optimal discriminative projection matrix. For this …

Successful application of microorganisms to heavy metal remediation depends on their
resistance to toxic metals. This study contrasted the differences of tolerant mechanisms …

Sparse representation has been widely studied as a part-based data representation method
and applied in many scientific and engineering fields, such as bioinformatics and medical …

In this article we propose several two-phase representation-based classification (RBC)
methods that are inspired by the idea of the two-phase test sample sparse representation …

Nonnegative matrix factorization (NMF), a popular part-based representation technique,
does not capture the intrinsic local geometric structure of the data space. Graph regularized …

Human action recognition is a hot research topic; however, the change in shapes, the high
variability of appearances, dynamitic background, potential occlusions in different actions …