Matrix Cofactorization for Joint Representation Learning and Supervised Classification - Application to Hyperspectral Image Analysis

@article{Lagrange2020MatrixCF,
  title={Matrix Cofactorization for Joint Representation Learning and Supervised Classification - Application to Hyperspectral Image Analysis},
  author={Adrien Lagrange and M. Fauvel and S. May and J. Bioucas-Dias and N. Dobigeon},
  journal={Neurocomputing},
  year={2020},
  volume={385},
  pages={132-147}
}
  • Adrien Lagrange, M. Fauvel, +2 authors N. Dobigeon
  • Published 2020
  • Computer Science, Engineering
  • Neurocomputing
  • Supervised classification and representation learning are two widely used classes of methods to analyze multivariate images. Although complementary, these methods have been scarcely considered jointly in a hierarchical modeling. In this paper, a method coupling these two approaches is designed using a matrix cofactorization formulation. Each task is modeled as a factorization matrix problem and a term relating both coding matrices is then introduced to drive an appropriate coupling. The link… CONTINUE READING

    References

    SHOWING 1-10 OF 70 REFERENCES
    Task-Driven Dictionary Learning for Hyperspectral Image Classification With Structured Sparsity Constraints
    • 58
    • PDF
    Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
    • 9
    • PDF
    Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
    • 946
    • PDF
    Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing
    • 419
    • PDF
    Enhancing Hyperspectral Image Unmixing With Spatial Correlations
    • 134
    • PDF
    Nonparametric Coupled Bayesian Dictionary and Classifier Learning for Hyperspectral Classification
    • N. Akhtar, A. Mian
    • Computer Science, Medicine
    • IEEE Transactions on Neural Networks and Learning Systems
    • 2018
    • 24
    Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability
    • 61
    • PDF
    Task-Driven Dictionary Learning
    • 804
    • PDF