Semi-supervised learning with summary statistics

Huihui Qin, Xin Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Pages (from-to)837-851
Number of pages15
JournalAnalysis and Applications
Volume17
Issue number5
DOIs
Publication statusPublished - 1 Sep 2019

Keywords

  • Distributed learning
  • empirical features
  • privacy protection
  • semi-supervised learning
  • summary statistics

ASJC Scopus subject areas

  • Analysis
  • Applied Mathematics

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