@CONFERENCE\{IMM2011-06078,
    author       = "M. M{\o}rup and M. N. Schmidt",
    title        = "Transformation Invariant Sparse Coding",
    year         = "2011",
    booktitle    = "Machine Learning for Signal Processing (MLSP), {IEEE} Workshop on",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6078-full.html",
    abstract     = "Sparse coding is a well established principle for unsupervised learning. Traditionally, features are extracted in sparse coding in specific locations, however, often we would prefer invariant representation. This paper introduces a general transformation invariant sparse coding (TISC) model. The model decomposes images into features invariant to location and general transformation by a set of specified operators as well as a sparse coding matrix indicating where and to what degree in the original image these features are present. The {TISC} model is in general overcomplete and we therefore invoke
sparse coding to estimate its parameters. We demonstrate how the model can correctly identify components of non-trivial artificial as well as real image data. Thus, the model is capable of reducing feature redundancies in terms of pre-specified transformations improving the component identification."
}