@InProceedings{8903037,
  author = {A. Lagrange and M. Fauvel and S. May and J. M. Bioucas-Dias and N. Dobigeon},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images},
  year = {2019},
  pages = {01-05},
  abstract = {This paper introduces a matrix cofactorization approach to perform spectral unmixing and classification jointly. After formulating the unmixing and classification tasks as matrix factorization problems, a link is introduced between the two coding matrices, namely the abundance matrix and the feature matrix. This coupling term can be interpreted as a clustering term where the abundance vectors are clustered and the resulting attribution vectors are then used as feature vectors. The overall non-smooth, non-convex optimization problem is solved using a proximal alternating linearized minimization algorithm (PALM) ensuring convergence to a critical point. The quality of the obtained results is finally assessed by comparison to other conventional algorithms on semi-synthetic yet realistic dataset.},
  keywords = {convergence of numerical methods;convex programming;feature extraction;image classification;iterative methods;least squares approximations;matrix decomposition;minimisation;joint unmixing;hyperspectral images;matrix cofactorization approach;unmixing classification;matrix factorization problems;abundance matrix;feature matrix;coupling term;clustering term;abundance vectors;feature vectors;nonconvex optimization problem;attribution vectors;proximal alternating linearized minimization algorithm;PALM algorithm;Optimization;Couplings;Task analysis;Indexes;Hyperspectral imaging;Clustering algorithms;supervised learning;spectral unmixing;cofactorization;hyperspectral images},
  doi = {10.23919/EUSIPCO.2019.8903037},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570531255.pdf},
}

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M.","Dobigeon, N."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["A."],"propositions":[],"lastnames":["Lagrange"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Fauvel"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["May"],"suffixes":[]},{"firstnames":["J.","M."],"propositions":[],"lastnames":["Bioucas-Dias"],"suffixes":[]},{"firstnames":["N."],"propositions":[],"lastnames":["Dobigeon"],"suffixes":[]}],"booktitle":"2019 27th European Signal Processing Conference (EUSIPCO)","title":"Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images","year":"2019","pages":"01-05","abstract":"This paper introduces a matrix cofactorization approach to perform spectral unmixing and classification jointly. After formulating the unmixing and classification tasks as matrix factorization problems, a link is introduced between the two coding matrices, namely the abundance matrix and the feature matrix. This coupling term can be interpreted as a clustering term where the abundance vectors are clustered and the resulting attribution vectors are then used as feature vectors. The overall non-smooth, non-convex optimization problem is solved using a proximal alternating linearized minimization algorithm (PALM) ensuring convergence to a critical point. 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Lagrange and M. Fauvel and S. May and J. M. Bioucas-Dias and N. Dobigeon},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images},\n year = {2019},\n pages = {01-05},\n abstract = {This paper introduces a matrix cofactorization approach to perform spectral unmixing and classification jointly. After formulating the unmixing and classification tasks as matrix factorization problems, a link is introduced between the two coding matrices, namely the abundance matrix and the feature matrix. This coupling term can be interpreted as a clustering term where the abundance vectors are clustered and the resulting attribution vectors are then used as feature vectors. The overall non-smooth, non-convex optimization problem is solved using a proximal alternating linearized minimization algorithm (PALM) ensuring convergence to a critical point. The quality of the obtained results is finally assessed by comparison to other conventional algorithms on semi-synthetic yet realistic dataset.},\n keywords = {convergence of numerical methods;convex programming;feature extraction;image classification;iterative methods;least squares approximations;matrix decomposition;minimisation;joint unmixing;hyperspectral images;matrix cofactorization approach;unmixing classification;matrix factorization problems;abundance matrix;feature matrix;coupling term;clustering term;abundance vectors;feature vectors;nonconvex optimization problem;attribution vectors;proximal alternating linearized minimization algorithm;PALM algorithm;Optimization;Couplings;Task analysis;Indexes;Hyperspectral imaging;Clustering algorithms;supervised learning;spectral unmixing;cofactorization;hyperspectral images},\n doi = {10.23919/EUSIPCO.2019.8903037},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570531255.pdf},\n}\n\n","author_short":["Lagrange, A.","Fauvel, M.","May, S.","Bioucas-Dias, J. 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