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• W2890413393 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 introduce Representative Dual Features (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." @default.
• W2890413393 created "2018-09-27" @default.
• W2890413393 creator A5012772285 @default.
• W2890413393 creator A5014196059 @default.
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• W2890413393 date "2019-02-01" @default.
• W2890413393 modified "2023-09-26" @default.
• W2890413393 title "Unsupervised Multi-task Learning with Hierarchical Data Structure" @default.
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• W2890413393 doi "https://doi.org/10.1016/j.patcog.2018.08.021" @default.
• W2890413393 hasPublicationYear "2019" @default.
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