Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88201
PIRA download icon_1.1View/Download Full Text
Title: Semi-supervised learning with summary statistics
Authors: Qin, H
Guo, X 
Issue Date: Sep-2019
Source: Analysis and applications, Sept. 2019, v. 17, no. 5, p. 837-851
Abstract: Nowadays, the extensive collection and analyzing of data is stimulating widespread privacy concerns, and therefore is increasing tensions between the potential sources of data and researchers. A privacy-friendly learning framework can help to ease the tensions, and to free up more data for research. We propose a new algorithm, LESS (Learning with Empirical feature-based Summary statistics from Semi-supervised data), which uses only summary statistics instead of raw data for regression learning. The selection of empirical features serves as a trade-off between prediction precision and the protection of privacy. We show that LESS achieves the minimax optimal rate of convergence in terms of the size of the labeled sample. LESS extends naturally to the applications where data are separately held by different sources. Compared with the existing literature on distributed learning, LESS removes the restriction of minimum sample size on single data sources.
Keywords: Distributed learning
Semi-supervised learning
Empirical features
Summary statistics
Privacy protection
Publisher: World Scientific
Journal: Analysis and applications 
ISSN: 0219-5305
EISSN: 1793-6861
DOI: 10.1142/S0219530519400037
Description: Title of accepted manuscript "On semi-supervised learning with summary statistics"
Rights: Electronic version of an article published as Analysis and Applications, vol. 17, no. 5, 2019, p. 837-851, https://doi.org/10.1142/S0219530519400037, © World Scientific Publishing Company, https://www.worldscientific.com/toc/aa/17/05
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
QinGuoRev1.pdfPre-Published version731.19 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

71
Citations as of May 15, 2022

Downloads

22
Citations as of May 15, 2022

SCOPUSTM   
Citations

1
Citations as of May 12, 2022

WEB OF SCIENCETM
Citations

1
Citations as of May 19, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.