Learning with Clustering Penalties
Vincent Roulet, Fajwel Fogel, Alexandre d’Aspremont, Francis Bach · arXiv (Cornell University) · 2015
We study supervised learning problems using clustering penalties to impose structure on either features, tasks or samples, seeking to help both prediction and interpretation. This arises naturally in problems involving dimensionality reduction, transfer learning or regression clustering. We derive a unified optimization formulation handling these three settings and produce algorithms whose core iteration complexity amounts to a k-means clustering step, which can be approximated efficiently. We test the robustness of our methods on artificial data sets as well as real data extracted from movie reviews and a corpus of text documents.