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A distributed semi-supervised learning algorithm based on manifold regularization using wavelet neural network.

Jin Xie1, Sanyang Liu1, Hao Dai2

  • 1School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.

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|July 23, 2019
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Summary
This summary is machine-generated.

A new distributed semi-supervised learning (D-SSL) algorithm, distributed LapWNN (D-LapWNN), uses Wavelet Neural Networks and a Zero-Gradient-Sum strategy for efficient, privacy-preserving learning on large, distributed datasets.

Keywords:
Distributed learning (DL)Manifold regularization (MR)Privacy preservingSemi-supervised learning (SSL)Wavelet neural network (WNN)

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Area of Science:

  • Machine Learning
  • Distributed Computing
  • Data Science

Background:

  • Distributed semi-supervised learning (D-SSL) addresses challenges with large-scale, decentralized datasets where central processing is infeasible.
  • Existing kernel-based D-SSL methods like D-LapRLS face computational bottlenecks due to global matrix estimation.

Purpose of the Study:

  • To propose a novel, efficient, and privacy-preserving D-SSL algorithm overcoming limitations of current kernel-based approaches.
  • To introduce a Manifold Regularization (MR) based D-SSL algorithm utilizing Wavelet Neural Networks (WNN) and a Zero-Gradient-Sum (ZGS) optimization strategy.

Main Methods:

  • Developed a centralized MR-based SSL algorithm using WNN for initialization (LapWNN).
  • Implemented a fully distributed D-LapWNN algorithm using WNN and ZGS strategy for efficient, privacy-preserving distributed learning.
  • Guaranteed convergence of D-LapWNN using Lyapunov method.

Main Results:

  • The proposed D-LapWNN algorithm demonstrates efficiency and privacy preservation by exchanging only local coefficients between nodes.
  • Simulations confirm the effectiveness and advantages of the D-LapWNN algorithm compared to existing methods.

Conclusions:

  • D-LapWNN offers a scalable and privacy-preserving solution for D-SSL problems with large, distributed datasets.
  • The integration of WNN and ZGS strategy provides a robust framework for decentralized machine learning.