  In machine learning it is common to interpret each data point as a vector in
Euclidean space. However the data may actually be functional i.e.\ each data
point is a function of some variable such as time and the function is
discretely sampled. The naive treatment of functional data as traditional
multivariate data can lead to poor performance since the algorithms are
ignoring the correlation in the curvature of each function. In this paper we
propose a tractable method to cluster functional data or curves by adapting the
Euclidean Low-Rank Representation (LRR) to the curve manifold. Experimental
evaluation on synthetic and real data reveals that this method massively
outperforms prior clustering methods in both speed and accuracy when clustering
functional data.
