Corpus ID: 235694247

SISAL Revisited

  title={SISAL Revisited},
  author={Chu-Hsiang Huang and Mingjie Shao and Wing-Kin Ma and Anthony Man-Cho So},
Simplex identification via split augmented Lagrangian (SISAL) is a popularly-used algorithm in blind unmixing of hyperspectral images. Developed by José M. Bioucas-Dias in 2009, the algorithm is fundamentally relevant to tackling simplex-structured matrix factorization, and by extension, non-negative matrix factorization, which have many applications under their umbrellas. In this article, we revisit SISAL and provide new meanings to this quintessential algorithm. The formulation of SISAL was… Expand

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