Unsupervised Multi-task Learning with Hierarchical Data Structure

Highlights：

• Overlapping between feature groups of different clusters is allowed to encourage the shared information and distinct information simultaneously;

• Representative Dual Features (RepDFs) is introduced to evaluate the correlations between clusters;

• Hierarchical structural similarities between clusters are explored in feature space and sample space from the topological perspective;

• Correlations from RepDFs are incorporated into hierarchical structural similarities to guide knowledge transfer, which increases diversities of instances by exploiting instances from related clusters.