Archetypal analysis (AA) proposed by Cutler and Breiman in  estimates the principal convex hull of a data set. As such AA favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - without being limited to hard assignment and the uniqueness of SVD - without being limited to orthogonal representations. In order to do large scale AA, we derive an efficient algorithm based on projected gradient as well as an initialization procedure inspired by the FURTHESTFIRST approach widely used for K-means . We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering.
|Title of host publication||IEEE International Workshop on Machine Learning for Signal Processing|
|Number of pages||172|
|Publication status||Published - 2010|
|Event||2010 IEEE International Workshop on Machine Learning for Signal Processing - Kittilä, Finland|
Duration: 29 Aug 2010 → 1 Sep 2010
|Workshop||2010 IEEE International Workshop on Machine Learning for Signal Processing|
|Period||29/08/2010 → 01/09/2010|