Unsupervised Learning of Sparse and Invariant Features Hierarchies

Unsupervised learning methods are commonly used to produce feature extractors in image analysis systems. A challenging question is whether these methods can learn invariant hierarchies of features. This would make much easier the problem of extracting useful informati on from very high dimensional datasets with few labeled samples, as it is often the case in many object rec ognition tasks in computer vision.