@CONFERENCE\{IMM2006-04865,
    author       = "J. Arenas-García and K. B. Petersen and L. K. Hansen",
    title        = "Sparse kernel orthonormalized {PLS} for feature extraction in large datasets",
    year         = "2006",
    month        = "dec",
    booktitle    = "{NIPS} 2006",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4865-full.html",
    abstract     = "In this paper we are presenting a novel multivariate analysis method for large
scale problems. Our scheme is based on a novel kernel orthonormalized partial
least squares (PLS) variant for feature extraction, imposing sparsity constrains
in the solution to improve scalability. The algorithm is tested on a benchmark
of {UCI} data sets, and on the analysis of integrated short-time music features for
genre prediction. The upshot is that the method has strong expressive power even
with rather few features, is clearly outperforming the ordinary kernel {PLS,} and
therefore is an appealing method for feature extraction of labelled data."
}