@CONFERENCE\{IMM2011-06082,
    author       = "M. M{\o}rup and M. N. Schmidt",
    title        = "Transformation invariant sparse coding",
    year         = "2011",
    booktitle    = "Machine Learning for Signal Processing, {IEEE} International Workshop on (MLSP)",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    organization = "",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6082-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."
}