The long-term safe operation of rotating machinery is closely related to the stability of rolling
bearings. This paper proposes a rolling bearing fault diagnosis method based on refined …

Subspace clustering aims to fit each category of data points by learning an underlying
subspace and then conduct clustering according to the learned subspace. Ideally, the …

Dimensionality reduction has many applications in data visualization and machine learning.
Existing methods can be classified into global ones and local ones. The global methods …

Epigenetic alterations have an important role in the development of several types of cancer.
Epigenetic studies generate a large amount of data, which makes it essential to develop …

We propose a new supervised dimensionality reduction technique called Supervised Linear
Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder …

Peak loads in higher education institutional building clusters (IBCs) possess considerable
economic repercussions on their overall operations. Thus, identifying electrically inefficient …

In the literature, metaheuristics are proposed as alternatives to traditional techniques such
as grid search, gradient descent, randomized search, and experimental methods in tuning …

As graph technology is widely used in unsupervised dimensionality reduction, many
methods automatically construct a full connection graph to learn the structure of data, and …

Purpose The concept of productivity is central to performance management and decision-
making, although it is complex and multifaceted. This paper aims to describe a methodology …

In a world of connectivity empowered by the advancement of the Internet of Things (IoT), an
infinite number of data streams have emerged. Thus, data stream clustering is crucial for …