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  1. On a minimum enclosing ball of a collection of linear subspaces. (Accepted to Linear Algebra and Its Applications)
    T. Marrinan
    , P.-A. Absil, and N. Gillis.
    arXiv preprint arXiv:2003.12455 (2020).
    Preprint PDF
    Code package available: Grassmannian Minimum Enclosing Ball (GMEB) code
  2. Hyperspectral unmixing with rare endmembers via minimax nonnegative matrix factorization. (Accepted)
    T. Marrinan and N. Gillis.
    Proc. IEEE 28th European Signal Processing Conference (EUSIPCO), (2020).
    Preprint PDF
    Code package available: minimax NMF code
  3. Determining the Dimension and Structure of the Subspace Correlated Across Multiple Data Sets.
    T. Hasija, T. Marrinan, C. Lameiro, and P. J. Schreier.
    Signal Processing (2020)
    https://doi.org/10.1016/j.sigpro.2020.107613
    Preprint PDF
    Code package available: (at SST group Github)
  4. Estimating the Number of Correlated Components Based on Random Projections.
    C. Lameiro, T. Hasija, T. Marrinan, and P. J. Schreier.
    Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2019): 5152-5156.
    PDF
  5. Complete Model Selection in Multiset Canonical Correlation Analysis.
    T. Marrinan
    , T. Hasija, C. Lameiro, and P. J. Schreier.
    Proc. IEEE 26th European Signal Processing Conference (EUSIPCO), (2018): 1082-1086.
    PDF
    Code package available: Complete Model Selection in MCCA code
  6. Constrained subspace estimation via convex optimization.
    I. Santamaria, J. Via, M. Kirby, T. Marrinan, C. Peterson, and L. Scharf.
    Proc. IEEE 25th European Signal Processing Conference (EUSIPCO), (2017): 1200-1204.
    Open Access PDF
  7. Grassmann, Flag, and Schubert Varieties in Applications.
    T. Marrinan
    .
    Doctoral dissertation, Colorado State University (2017).
    Retrieved from http://hdl.handle.net/10217/181430
    Open Access PDF
  8. Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis.
    E. Jurrus, N. Hodas, N. Baker, T. Marrinan, and M. Hoover.
    Proc. IEEE Symposium on Technologies for Homeland Security (HST), (2016).
  9. Flag-based Detection of Weak Gas Signatures in Long-Wave Infrared Hyperspectral Image Sequences.
    T. Marrinan, J.R. Beveridge, B. Draper, M. Kirby and C. Peterson.
    Proc. SPIE 9840: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII. (2016).
    http://dx.doi.org/10.1117/12.2224117
    Preprint PDF
  10. Flag Manifolds for the Characterization of Geometric Structure in Large Data Sets.
    T. Marrinan, J. R. Beveridge, B. Draper, M. Kirby, and C. Peterson.
    Proc. Numerical Mathematics and Advanced Applications-ENUMATH 2013. 103 (2015): 457-465.
    http://dx.doi.org/10.1007/978-3-319-10705-9_45
    Open Access PDF
  11. Finding the subspace mean or median to fit your need.
    T. Marrinan, J. R. Beveridge, B. Draper, M. Kirby, and C. Peterson.
    Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2014): 1082-1089.
    http://dx.doi.org/10.1109/CVPR.2014.142
    Open Access PDF
    Code package available: Subspace Mean and Median Toolkit (SuMMeT)
  12. A Flag Representation for Finite Collections of Subspaces of Mixed Dimensions.
    B. Draper, M. Kirby, J. Marks, T. Marrinan, and C. Peterson.
    Linear Algebra and its Applications 451 (2014): 15-32.
    http://dx.doi.org/10.1016/j.laa.2014.03.022
    Open Access PDF
  13. The Flag of Best Fit as a Representative for a Collection of Linear Subspaces.
    T. Marrinan.
    Master’s Thesis, Colorado State University (2013).
    Retrieved from http://hdl.handle.net/10217/81042
    Open Access PDF