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Adaptive non-negative projective semi-supervised learning for inductive classification.

Zhao Zhang1, Lei Jia1, Mingbo Zhao2

  • 1School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 10, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces Adaptive Non-negative Projective Semi-Supervised Learning (ANP-SSL) for accurate inductive classification. ANP-SSL improves predictions by learning adaptive weights and propagating labels using local data representations, outperforming existing methods.

Keywords:
Adaptive projective semi-supervised learningInductive label propagationNon-negative matrix factorizationRepresentation and classification

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Area of Science:

  • Machine Learning
  • Computer Vision
  • Data Mining

Background:

  • Inductive classification presents challenges in accurately predicting labels for unseen data.
  • Existing methods often rely on noisy original data representations and pre-assigned weights, limiting classification accuracy.
  • There is a need for adaptive frameworks that learn optimal weights and leverage robust data representations.

Purpose of the Study:

  • To propose a novel joint framework, Adaptive Non-negative Projective Semi-Supervised Learning (ANP-SSL), for enhanced inductive classification.
  • To integrate adaptive label propagation, adaptive reconstruction weights learning, and neighborhood preserving projective nonnegative matrix factorization (PNMF).
  • To improve classification accuracy by minimizing errors in semi-supervised data representation and classification.

Main Methods:

  • ANP-SSL combines adaptive inductive label propagation and adaptive reconstruction weights learning with neighborhood preserving PNMF.
  • It incorporates semi-supervised data representation and classification errors into PNMF minimization.
  • The framework learns adaptive weights and propagates labels over spatially local, part-based data representations.

Main Results:

  • ANP-SSL enables adaptive weights learning and label propagation on robust, local data representations, unlike methods using original, potentially noisy data.
  • Joint minimization of representation and classification errors leads to improved part-based representations and enhanced prediction results.
  • Extensive experiments on public image databases demonstrate the effectiveness of ANP-SSL compared to state-of-the-art methods.

Conclusions:

  • ANP-SSL offers a superior approach to inductive classification by integrating adaptive learning mechanisms and robust data representations.
  • The joint optimization strategy effectively enhances classification performance and the quality of learned data embeddings.
  • The framework's ability to adapt weights and propagate labels based on local features provides a significant advantage over traditional methods.