@CONFERENCE\{IMM2009-05837,
    author       = "M. Arngren and M. N. Schmidt and J. Larsen",
    title        = "Bayesian Nonnegative Matrix Factoization with Volume Constraints for Unmixing af Hyperspectral Images",
    year         = "2009",
    month        = "sep",
    keywords     = "Bayesian, volume constraint, Gibbs sampling, {NMF,} Hyperspectral images",
    booktitle    = "2009 {IEEE} International Workshop on Machine Learning for Signal Processing ({MLSP} 2009)",
    volume       = "",
    series       = "",
    editor       = "T. Adali, J. Chanussot, C. Jutten, J. Larsen",
    publisher    = "{IEEE} Press",
    organization = "",
    address      = "",
    note         = "{DOI} 10.1109/MLSP.2009.5306262",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5837-full.html",
    abstract     = "In hyperspectral image analysis the objective is to unmix
a set of acquired pixels into pure spectral signatures (endmembers)
and corresponding fractional abundances. The
Non-negativeMatrix Factorization (NMF) methods have received
a lot of attention for this unmixing process. Many of
these {NMF} based unmixing algorithms are based on sparsity
regularization encouraging pure spectral endmembers,
but this is not optimal for certain applications, such as foods,
where abundances are not sparse. The pixels will theoretically
lie on a simplex and hence the endmembers can be estimated
as the vertices of the smallest enclosing simplex. In
this context we present a Bayesian framework employing a
volume constraint for the {NMF} algorithm, where the posterior
distribution is numerically sampled from using a Gibbs
sampling procedure. We evaluate the method on synthetical
and real hyperspectral data of wheat kernels.",
    isbn_issn    = "{ISBN} 9781424449477"
}