The classification performance of multi-view learning is better than the traditional machine
learning algorithms as stated in state-of-art. In this research, using the graph coloring …

Multi-view ensemble learning (MEL) is new and fast growing area of machine learning.
Here, the subsets of multiple features of same dataset are used for the learning and their …

In recent years, multiview learning technologies have attracted a surge of interest in the
machine learning domain. However, when facing complex and diverse applications, most …

In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

In recent years, multi-view learning methods have developed rapidly where graph-based
approaches have achieved good performance. Usually, these learning methods construct …

Multi-view learning attempts to generate a model with a better performance by exploiting
information among multi-view data. Most existing approaches only focus on either …

Multi-view clustering is a long-standing important task, however, it remains challenging to
exploit valuable information from the complex multi-view data located in diverse high …

Existing multi-view graph learning methods often rely on consistent information for similar
nodes within and across views, however they may lack adaptability when facing diversity …

Multi-view learning techniques have recently gained significant attention in the machine
learning domain for their ability to leverage consistency and complementary information …

With the increase popularity of multi-view data, multi-view learning has attracted vital
attentions in pattern recognition as well as machine learning. Most of existing methods apply …