Exploring Correlations Among Tasks, Clusters, and Features for Multitask Clustering

Multitask clustering methods are proposed to improve performances of related tasks concurrently, because they explore the relationship among tasks via exploiting the coefficient matrix or the shared feature matrix. However, divergent effects of features in learning this relationship are seldom considered. To further improve performances, we propose a new multitask clustering approach through exploring correlations among tasks, clusters, and features based on effects of features on clusters. First, a Feature-Cluster (FeaCluster) matrix is introduced to capture the similarity and the distinct task-feature information simultaneously for each task. With the FeaCluster matrix, two affinities are calculated to constitute the interdependencies among tasks: the former is the graphical affinity based on feature-task and task-cluster correlations, while the latter is the reconstructive affinity. Here, the feature-task correlation considers effects of features on tasks, and the task-cluster correlation considers the overall effects of features on clusters. The reconstructive affinity is obtained by minimizing the reconstruction error when representing the FeaCluster matrix for a given task with a linear combination of others. The interdependencies among tasks allow transferring asymmetric shared information, exploring significant features and preserving key information when mapping data into the subspace. The experimental results on multiple data sets reveal that the proposed approach outperforms the state-of-theart clustering methods in terms of accuracy and normal mutual information.