@article {PUBA102,
	title = {Sparse Coding of Human Motion Trajectories with Non-negative Matrix Factorization},
	author = {Christian Vollmer AND Sven Hellbach AND Julian Eggert AND Horst-Michael Gro{\ss}},
	year = {2014},
	abstract = {We use shift-invariant Non-negative Matrix Factorization (NMF) for decom-
posing continuous-valued time series into a number of characteristic primi-
tives, i.e. the basis vectors, and their activations, which results in a model-
independent and fully data driven parts-based representation. We interpret
the basis vectors as short parts of motion that are shared between all trajec-
tories in the data set, and the activations as onset times of those parts. The
extension of the shift-invariant NMF by a new competition term between ad-
jacent activations allows to gain temporally isolated activation events, which
further supports this interpretation. We show that the resulting sparse and
compact representation can be used for the prediction of motion trajectories,
and that it can be benecial for classication, because it allows the appli-
cation of simple standard classication models with few parameters. In this
paper we show that basis vectors can be extracted, which can be interpreted
as short motion segments. We present results on trajectory prediction, and
show that the sparse representation can be used for classication of trajec-
tories of a single joint, like the one of a hand, obtained by motion capturing.},
	publisher = {NC},
	journal = {Neurocomputing},
	pages = {22-32}
}