Consistent Multigroup Low-Rank Approximation

Matakos, Antonis, Ciaperoni, Martino, Mannila, Heikki

arXiv.org Machine Learning 

We consider the problem of consistent low-rank approximation for multigroup data: we ask for a sequence of $k$ basis vectors such that projecting the data onto their spanned subspace treats all groups as equally as possible, by minimizing the maximum error among the groups. Additionally, we require that the sequence of basis vectors satisfies the natural consistency property: when looking for the best $k$ vectors, the first $d