The advent of large-scale microbiome studies affords newfound analytical opportunities to
understand how these communities of microbes operate and relate to their environment …

Pattern analysis often requires a pre-processing stage for extracting or selecting features in
order to help the classification, prediction, or clustering stage discriminate or represent the …

Dimensionality reduction methods, also known as projections, are frequently used in
multidimensional data exploration in machine learning, data science, and information …

Dimensionality reduction aims to map the high-dimensional inputs onto a low-dimensional
subspace, in which the similar points are close to each other and vice versa. In this paper …

Biomedical scientific literature is growing at a very rapid pace, which makes increasingly
difficult for human experts to spot the most relevant results hidden in the papers …

Recent works have proposed two L1-norm distance measure-based linear discriminant
analysis (LDA) methods, L1-LD and LDA-L1, which aim to promote the robustness of the …

During the last decade, the deluge of multimedia data has impacted a wide range of
research areas, including multimedia retrieval, 3D tracking, database management, data …

Dimensionality reduction methods, also known as projections, are often used to explore
multidimensional data in machine learning, data science, and information visualization …

In the past few years, augmented reality (AR) and virtual reality (VR) technologies have
experienced terrific improvements in both accessibility and hardware capabilities …

Topic models are a class of unsupervised learning algorithms for detecting the semantic
structure within a text corpus. Together with a subsequent dimensionality reduction …