Multiview graph clustering has emerged as an important yet challenging technique due to
the difficulty of exploiting the similarity relationships among multiple views. Typically, the …

Manifold alignment is very prevalent in machine learning for extracting common latent space
from multiple datasets. These algorithms generally aim to achieve higher alignment …

The advantages of hybrid electric vehicle (HEV) in energy-saving strongly rely on the energy
management strategy. Equivalent consumption minimization strategy (ECMS) is one kind of …

Abstract Support Vector Machine (SVM) is widely applied in classification and regression
tasks where support vectors are pursued through convex quadratic programming technique …

Dimensionality reduction plays a key role in pattern recognition. It can preserve essential
and inherent feature information while reducing noise and redundant information contained …

Dimensionality reduction is widely used to visualize complex high-dimensional data. This
study presents a novel method for effective data visualization. Previous methods depend on …

Principal component analysis is a widely used technique. However, it is sensitive to noise
and considers data samples to be linearly distributed globally. To tackle these challenges, a …

Cross-domain data has become very popular recently since various viewpoints and different
sensors tend to facilitate better data representation. In this article, we propose a novel cross …

This paper considers unsupervised dimensionality reduction of multi-view data, where
locality preserving canonical correlation analysis and a new locality-preserving canonical …

In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-
based locality preserving projections (EGLPP); to overcome the neighborhood size k …