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Joint sparse graph and flexible embedding for graph-based semi-supervised learning.

F Dornaika1, Y El Traboulsi2

  • 1University of the Basque Country UPV/EHU, San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.

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

This study presents a new graph-based semi-supervised learning framework. It jointly estimates graph structure, non-linear projection, and regression for improved performance in image datasets.

Keywords:
Discriminant embeddingGraph constructionGraph-based embeddingInductive modelNon-linear projectionSemi-supervised learning

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Semi-supervised learning leverages limited labeled data for model training.
  • Existing methods often estimate graph structure and regression models separately.
  • This can lead to suboptimal performance due to unaddressed dependencies.

Purpose of the Study:

  • To introduce a novel framework for graph-based semi-supervised learning.
  • To enable joint estimation of graph structure, non-linear projection, and linear regression.
  • To achieve overall optimality and improved performance compared to existing methods.

Main Methods:

  • A flexible non-linear projection is estimated.
  • A linear regression model is integrated with the projection.
  • Graph structure, projection, and regression are jointly optimized.
  • Experiments were conducted on five image datasets.

Main Results:

  • The proposed framework demonstrates effectiveness in semi-supervised learning.
  • It outperforms state-of-the-art semi-supervised methods.
  • Joint estimation of graph and soft labels showed inferior results compared to the proposed framework.

Conclusions:

  • The proposed joint estimation framework offers superior performance in semi-supervised learning.
  • This approach enhances embedding methods for image datasets.
  • It provides a more optimal solution by considering interdependencies.