Dimensionality reduction is standard practice for filtering noise and identifying relevant
features in large-scale data analyses. In biology, single-cell genomics studies typically begin …

Image retrieval is one of the hot research topics in computer vision which has been paid
much attention by researchers in the last decade. Image retrieval refers to retrieving more …

Distance-based classification is frequently used in transductive few-shot learning (FSL).
However, due to the high-dimensionality of image representations, FSL classifiers are prone …

This paper presents a novel adaptive sequential sampling method for building Polynomial
Chaos Expansion surrogate models. The technique enables one-by-one extension of an …

Data samples of complicated geometry and nonlinear separability are considered as
common challenges to clustering algorithms. In this article, we first construct Mahalanobis …

Feature selection has become an indispensable part of intelligent systems, especially with
the proliferation of high dimensional data. It identifies the subset of discriminative features …

The K-means algorithm is among the most commonly used data clustering methods.
However, the regular K-means can only be applied in the input space, and it is applicable …

Data are often represented as graphs. Many common tasks in data science are based on
distances between entities. While some data science methodologies natively take graphs as …

Parkinson's disease (PD) is the world's second most common progressive
neurodegenerative disease. This disease is characterized by a combination of various non …

An innovative strategy for organizations to obtain value from their large datasets, allowing
them to guide future strategic actions and improve their initiatives, is the use of machine …