Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization

@article{Lin2018MaximumVI,
  title={Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization},
  author={Chia-Hsiang Lin and Ruiyuan Wu and Wing-Kin Ma and Chong-Yung Chi and Yue Wang},
  journal={SIAM J. Imaging Sciences},
  year={2018},
  volume={11},
  pages={1651-1679}
}
Consider a structured matrix factorization model where one factor is restricted to have its columns lying in the unit simplex. This simplex-structured matrix factorization (SSMF) model and the associated factorization techniques have spurred much interest in research topics over different areas, such as hyperspectral unmixing in remote sensing and topic discovery in machine learning, to name a few. In this paper we develop a new theoretical SSMF framework whose idea is to study a maximum volume… CONTINUE READING

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