Group-invariant Subspace Clustering


In this paper we consider the problem of group-invariant subspace clustering where the data is assumed to come from a union of group-invariant subspaces of a vector space, i.e. subspaces which are invariant with respect to action of a given group. Algebraically, such group-invariant subspaces are also referred to as submodules. Similar to the well known Sparse Subspace Clustering approach where the data is assumed to come from a union of subspaces, we analyze an algorithm which, following a recent work [1], we refer to as Sparse Sub-module Clustering (SSmC). The method is based on finding group-sparse self-representation of data points. In this paper we primarily derive general conditions under which such a group-invariant subspace identification is possible. In particular we extend the geometric analysis in [2] and in the process we identify a related problem in geometric functional analysis.

DOI: 10.1109/ALLERTON.2015.7447068

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@article{Aeron2015GroupinvariantSC, title={Group-invariant Subspace Clustering}, author={Shuchin Aeron and Eric Kernfeld}, journal={2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)}, year={2015}, pages={666-671} }