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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,, © World Scientific Publishing Company,
Appears in Collections:Journal/Magazine Article

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