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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning.

Jinrong Cui1, Yazi Xie1, Chengliang Liu2

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces LSGC, a novel method for dual incomplete multi-view multi-label classification. LSGC effectively handles missing views and labels by leveraging label semantics and contrastive learning for improved accuracy.

Keywords:
Contrastive learningDeep learningDual incomplete multi-view multi-label classificationLabel correlationsPseudo-label

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-view multi-label learning (MVML) typically assumes complete data, limiting its real-world applicability.
  • Existing methods struggle with datasets containing both missing views and uncertain labels.
  • Prior work often overlooks or inadequately utilizes latent label information.

Purpose of the Study:

  • To propose a novel method, LSGC, for dual incomplete multi-view multi-label classification.
  • To address the limitations of existing MVML approaches in handling missing data.
  • To effectively exploit label semantics and enhance feature representation learning.

Main Methods:

  • LSGC employs deep neural networks for feature extraction.
  • A graph convolutional network is utilized to capture label semantics and correlations.
  • A sample-label contrastive loss enhances feature learning, complemented by a pseudo-label strategy for missing labels.

Main Results:

  • LSGC demonstrates superior performance on five standard datasets.
  • The method effectively handles dual incomplete multi-view multi-label classification challenges.
  • Experimental results confirm the effectiveness of leveraging label semantics and contrastive learning.

Conclusions:

  • LSGC offers a robust solution for incomplete MVML problems.
  • The proposed method advances the state-of-the-art in multi-view multi-label classification.
  • LSGC provides a promising direction for handling complex, real-world data scenarios.