2018 • Conference Paper
A Grassmannian Minimum Enclosing Ball Approach for Common Subspace Extraction
Authors:
Renard, Emilie,
Gallivan, Kyle A.,
Absil, Pierre-Antoine
Published in:
LVA ICA 2018
We study the problem of finding a subspace representative of multiple datasets by minimizing the maximal dissimilarity between this subspace and all the subspaces generated by those datasets. After arguing for the choice of the dissimilarity function, we derive some properties of the corresponding formulation. We propose an adaptation of an algorithm used for a similar problem on Riemannian manifolds. Experiments on synthetic data show that the subspace recovered by our algorithm is closer to the true common subspace than the solution obtained using an SVD.