@CONFERENCE\{IMM2010-05912,
    author       = "M. M{\o}rup and L. K. Hansen",
    title        = "Archetypal Analysis for Machine Learning",
    year         = "2010",
    booktitle    = "Machine Learning for Signal Processing (MLSP), {IEEE} Workshop on",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5912-full.html",
    abstract     = "Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such {AA} favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will
show that {AA} enjoys the interpretability of clustering - without being limited to hard assignment and the uniqueness of {SVD} - without being limited to orthogonal representations. In order to do large scale {AA,} we derive an efficient algorithm based on projected gradient as well as an initialization procedure inspired by the {FURTHESTFIRST} approach widely used for {K-}means [2]. We demonstrate that the {AA} model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems
taken from computer vision, neuroimaging, text mining and collaborative filtering."
}