Abstract

Functional data, where samples are random functions, are increasingly common and important in a variety of applications, such as health care and traffic analysis. They are naturally high dimensional and lie along complex manifolds. These properties warrant use of the subspace assumption, but most state-of-the-art subspace learning algorithms are limited to linear or other simple settings. To address these challenges, we propose a new framework called Functional Subspace Clustering (FSC). FSC assumes that functional samples lie in deformed linear subspaces and formulates the subspace learning problem as a sparse regression over operators. The resulting problem can be efficiently solved via greedy variable selection, given access to a fast deformation oracle. We provide theoretical guarantees for FSC and show how it can be applied to time series with warped alignments. Experimental results on both synthetic data and real clinical time series show that FSC outperforms both standard time series clustering and state-of-theart subspace clustering.