Abstract Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality
that makes machine learning algorithms incompetent. Our study conceptually and …

In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems
have gained considerable interest in high-value manufacturing industries to cope with the …

Visual analysis of multidimensional data requires expressive and effective ways to reduce
data dimensionality to encode them visually. Multidimensional projections (MDP) figure …

We propose a novel approach for preserving topological structures of the input space in
latent representations of autoencoders. Using persistent homology, a technique from …

This open access book covers aspects of unsupervised machine learning used for
knowledge discovery in data science and introduces a data-driven approach to cluster …

This work addresses recent developments for solving problems in process systems
engineering based on machine learning algorithms. A general description of most popular …

This paper explores dimensionality reduction (DR) approaches for visualizing high
dimensional data in chemical processes. Visualization provides powerful insight and …

Large-scale optimization problems present significant challenges due to the high
dimensionality of the search spaces and the extensive computational resources required …

Honey quality is a global concern since this product is highly susceptible to adulteration,
given its competitive price. As a reliable strategy for honey authenticity determination, this …

Dimensionality reduction methods can be used to project high-dimensional data into low-
dimensional space. If the output space is restricted to two dimensions, the result is a scatter …