Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

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 …

UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied
Riemannian geometry and algebraic topology to find low-dimensional embeddings of …

Efficient learning of a data analysis task strongly depends on the data representation. Most
methods rely on (symmetric) similarity or dissimilarity representations by means of metric …

Ink analysis is an important tool in forensic science and document analysis. Hyperspectral
imaging (HSI) captures large number of narrowband images across the electromagnetic …

This paper investigates the theoretical foundations of the t-distributed stochastic neighbor
embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data …

Lumbar degenerative disease (LDD) refers to the nerve compression syndrome such as
neurogenic intermittent claudication and lower limb pain, which disturbs people's daily life …

Unveiling consumer preferences in online reviews is receiving increasing attention. While
most existing approaches for consumer preferences have achieved significant …

Dimensionality reduction, also known as manifold learning, is an area of machine learning
used for extracting informative features from data for better representation of data or …

This study assesses the potential of Uniform Manifold Approximation and Projection (UMAP)
as an alternative tool to t-distributed Stochastic Neighbor Embedding (t-SNE) for the …