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Related Experiment Videos

Graph construction using adaptive Local Hybrid Coding scheme.

Fadi Dornaika1, Mahdi Tavassoli Kejani2, Alireza Bosaghzadeh3

  • 1University of the Basque Country, UPV/EHU, Manuel Lardizabal 1, 20018 San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Maria Diza de Haro, 3, 48013 Bilbao, Spain.

Neural Networks : the Official Journal of the International Neural Network Society
|September 22, 2017
PubMed
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Adaptive Local Hybrid Coding (ALHC) improves machine learning by combining sparsity and data locality. This new coding scheme enhances graph construction for better classification performance on face datasets.

Area of Science:

  • Computer Science
  • Machine Learning
  • Pattern Recognition

Background:

  • Dense coding methods can cause significant quantization errors and reduce machine learning performance.
  • Sparse coding prioritizes accurate data representation but often overlooks data locality and intrinsic structures.
  • Local Hybrid Coding (LHC) was introduced to merge sparsity and basis-locality criteria, offering advantages over traditional methods.

Purpose of the Study:

  • To introduce a novel data-driven graph construction method extending the Local Hybrid Coding (LHC) scheme.
  • To propose an Adaptive Local Hybrid Coding (ALHC) scheme that enhances representation by adaptively selecting bases.
  • To evaluate the effectiveness of ALHC in graph-based label propagation for improved classification performance.

Main Methods:

Keywords:
ClassificationGraph constructionLabel propagationLocal Hybrid CodeSparse coding

Related Experiment Videos

  • Developed an Adaptive Local Hybrid Coding (ALHC) scheme that dynamically selects local and non-local bases.
  • Utilized data similarities from Locality-constrained Linear Coding to inform adaptive base selection in ALHC.
  • Applied the proposed ALHC scheme for graph construction in graph-based label propagation tasks.

Main Results:

  • The proposed ALHC scheme effectively exploits local data similarities within its solutions.
  • ALHC demonstrated superior classification performance compared to existing methods on benchmark face datasets.
  • High classification accuracy was achieved on Extended Yale, PF01, PIE, and FERET datasets using the ALHC-based graph method.

Conclusions:

  • The Adaptive Local Hybrid Coding (ALHC) scheme offers a robust approach to combining sparsity and locality for data representation.
  • ALHC-based graph construction significantly enhances performance in graph-based label propagation tasks.
  • The proposed method shows strong potential for applications in image classification and pattern recognition.