Item Infomation
Title: | Fast Communication-efficient Spectral Clustering Over Distributed Data |
Authors: | Yan, Donghui |
Participants: | Wang, Yingjie Wang, Jin Wu, Guodong Wang, Honggang |
Issue Date: | 2019 |
Publisher: | IEEE Xplore |
Series/Report no.: | IEEE TRANSACTIONS ON BIG DATA (UNDER REIEW), (2019), pp 12 |
Abstract: | The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume all the data are already in one place, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with “minimal” communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing; the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while a 2x speedup under all settings we have explored. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing. |
URI: | http://tailieuso.tlu.edu.vn/handle/DHTL/9837 |
Source: | https://doi.org/10.1109/TBDATA.2019.2907985 |
Appears in Collections: | Tài liệu hỗ trợ nghiên cứu khoa học |
ABSTRACTS VIEWS
13
VIEWS & DOWNLOAD
2
Files in This Item:
Bạn đọc là cán bộ, giáo viên, sinh viên của Trường Đại học Thuỷ Lợi cần đăng nhập để Xem trực tuyến/Tải về
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.