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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation.

Najmeh Ziraki1, Fadi Dornaika2, Alireza Bosaghzadeh1

  • 1Shahid Rajaee Teacher Training University, Tehran, Iran.

Neural Networks : the Official Journal of the International Neural Network Society
|December 9, 2021
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Summary
This summary is machine-generated.

This study introduces a novel Multiple-View Consistent Graph construction and Label propagation (MVCGL) algorithm. MVCGL enhances data representation by simultaneously building graphs from multiple descriptors and propagating labels, improving accuracy in machine learning tasks.

Keywords:
Graph constructionGraph-based data smoothnessInformation fusionMulti-view semi-supervised classification

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Area of Science:

  • Machine Learning
  • Computer Vision
  • Data Mining

Background:

  • Graph construction is crucial for graph-based label propagation, providing data manifold structure.
  • Traditional methods often rely on predefined distance metrics, limiting flexibility.
  • Recent advancements integrate graph construction with label propagation, utilizing multiple descriptors for improved relational representation.

Purpose of the Study:

  • To propose a Multiple-View Consistent Graph construction and Label propagation (MVCGL) algorithm.
  • To simultaneously construct a consistent graph using multiple descriptors and perform label propagation.
  • To develop a mapping function for estimating labels of unseen samples.

Main Methods:

  • The MVCGL algorithm constructs a graph without a predefined similarity function, leveraging data and label smoothness.
  • It integrates multiple descriptors to represent relationships between data nodes.
  • A linear projection is used to map from feature space to label space for unseen sample label estimation.

Main Results:

  • Experiments on face and handwritten digit databases demonstrated superior performance of MVCGL.
  • The proposed method outperformed existing graph construction and label propagation techniques.
  • Simultaneous graph construction and label propagation using multiple views proved effective.

Conclusions:

  • The MVCGL algorithm offers an effective approach for simultaneous graph construction and label propagation.
  • Utilizing multiple descriptors enhances the representation of data relationships.
  • The method achieves improved performance in semi-supervised learning tasks, particularly for image datasets.