Archetypal analysis for machine learning

Morten Morup, Lars Kai Hansen
2010 2010 IEEE International Workshop on Machine Learning for Signal Processing  
Archetypal analysis (AA) proposed by Cutler and Breiman in [1] 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
more » ... gradient as well as an initialization procedure inspired by the FURTHESTFIRST approach widely used for K-means [2] . 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.
doi:10.1109/mlsp.2010.5589222 fatcat:d7un7jsmprayfgbbjkckzed66q