Transformation Invariant Sparse Coding
|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.
|Type||Conference paper [With referee]|
|Conference||Machine Learning for Signal Processing (MLSP), IEEE Workshop on|
|BibTeX data|| [bibtex]|
|IMM Group(s)||Intelligent Signal Processing|