Multi-view clustering and multi-task clustering attract much attention in recent years. With the
development of data mining, a new learning scenario containing the properties of multi-task …

Particle swarm optimization algorithm has become a promising approach in solving
multitask optimization (MTO) problems since it can transfer knowledge with easy …

In recent years, spectral clustering has become a well-known and effective algorithm in
machine learning. However, traditional spectral clustering algorithms are designed for single …

Multitask image clustering approaches intend to improve the model accuracy on each task
by exploring the relationships of multiple related image clustering tasks. However, most …

Direct multi-task twin support vector machine (DMTSVM) explores the shared information
between multiple correlated tasks, then it produces better generalization performance …

In this study, we develop a method for multi-task manifold learning. The method aims to
improve the performance of manifold learning for multiple tasks, particularly when each task …

In recent years, subspace clustering and multi-task clustering have received extensive
attention due to their wide practical applications. Traditional subspace clustering is limited to …

Transfer learning methods exploit similarities between different datasets to improve the
performance of the target task by transferring knowledge from source tasks to the target …

Multi-task clustering (MTC) has attracted a lot of research attentions in machine learning due
to its ability in utilizing the relationship among different tasks. Despite the success of …

Accurate and efficient resource allocation based on user intent is an important issue in a
large-scale, distributed environment, such as cloud computing. Although a large number of …