@CONFERENCE\{IMM2006-04042,
    author       = "K. Sj{\"{o}}strand and M. B. Stegmann and R. Larsen",
    title        = "Sparse Principal Component Analysis in Medical Shape Modeling",
    year         = "2006",
    month        = "feb",
    keywords     = "Sparse Principal Component Analysis, {PCA,} Evaluation of Principal Components, Shape Modeling, Corpus Callosum",
    booktitle    = "International Symposium on Medical Imaging 2006, San Diego, {CA,} {USA}",
    volume       = "6144",
    series       = "",
    editor       = "",
    publisher    = "The International Society for Optical Engineering (SPIE)",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4042-full.html",
    abstract     = "Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While {PCA} is a holistic approach where each new variable is a linear combination of all original variables, sparse {PCA} (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new
variable is a linear combination of a subset of the original variables. One of the aims of using {SPCA} is the possible separation of the results into isolated and easily identifiable effects.
This article introduces {SPCA} for shape analysis in medicine. Results for three different data sets are given in relation to standard {PCA} and sparse {PCA} by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The {SPCA} algorithm has been implemented using Matlab and is available for download.
The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the {SPCA} algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail."
}