Unsupervised Multi-task Learning with Hierarchical Data Structure. (February 2019)

• Record Type: Journal Article
• Authors: Cao, Wenming; Qian, Sheng; Wu, Si; Wong, Hau-San
• Is Part Of: Pattern recognition. Volume 86(2019:Feb.)
• Page Start: 248
• Page End: 264
• Publication Date: 2019-02
• Subjects: Multi-task learning -- hierarchical structure -- unsupervised learning -- structural similarity,; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.08.021
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: Unsupervised multi-task learning exploits the shared knowledge to improve performances by learning related tasks simultaneously. In this paper, we propose an unsupervised multi-task learning method with hierarchical data structure. It strengthens similarities between instances in the same cluster, and increases diversities of instances by utilizing instances from related clusters. Firstly, we introduceRep resentativeD ualF eatures (RepDFs) that possess representative capabilities in the feature space and the sample space for each cluster concurrently. Secondly, we explore hierarchical structural similarities between clusters in related tasks from the topological perspective: 1) feature basis matrix, which learns compact representations for features in the feature space; and 2) sample refined matrix, which preserves local structures in the sample space. Thirdly, we adopt RepDFs to measure correlations between clusters and incorporate hierarchical structural similarities to conduct knowledge transfer among tasks. Experimental results on real-world data sets demonstrate the effectiveness and superiority of the proposed method over existing multi-task clustering methods.