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Robust multi-view locality preserving regression embedding.

Ling Jing1,2,3, Yi Li2, Hongjie Zhang4

  • 1College of Science, China Agricultural University, Beijing, China.

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This study introduces novel multi-view feature extraction frameworks using regression embedding. These methods enhance single-view graph embedding (GE) for richer data, ensuring robustness against noise.

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Feature extractionGraph embeddingMulti-view learningRegression embedding

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Single-view graph embedding (GE) methods leverage structural information for feature extraction.
  • Multi-view data offers richer insights from diverse perspectives but lacks comprehensive extraction frameworks.
  • Increasing research interest highlights the need for advanced multi-view feature extraction techniques.

Purpose of the Study:

  • To propose innovative multi-view feature extraction frameworks.
  • To extend effective single-view graph embedding methods to multi-view scenarios.
  • To address the consistency, complementarity, and robustness of multi-view data.

Main Methods:

  • Developed three novel multi-view feature extraction frameworks based on regression embedding.
  • Extended existing single-view graph embedding techniques to handle multi-view data.
  • Employed non-linear shared embedding to preserve information and enhance robustness.

Main Results:

  • Validated the effectiveness of the proposed frameworks through numerical experiments.
  • Demonstrated the robustness of the frameworks on both real and noisy datasets.
  • Showcased the ability of non-linear embedding to prevent information loss compared to linear methods.

Conclusions:

  • The proposed regression embedding frameworks effectively perform multi-view feature extraction.
  • The methods are robust to noisy data and preserve essential information.
  • These frameworks offer a significant advancement for multi-view data analysis.