کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11030060 1646392 2019 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Unsupervised Multi-task Learning with Hierarchical Data Structure
ترجمه فارسی عنوان
صرفه جویی در یادگیری چند کاره با ساختار اطلاعات سلسله مراتبی
کلمات کلیدی
یادگیری چند کاره ساختار سلسله مراتبی، یادگیری بی نظیر، شباهت ساختاری ،،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 86, February 2019, Pages 248-264
نویسندگان
, , , ,