  Recently, transfer subspace learning based approaches have shown to be a
valid alternative to unsupervised subspace clustering and temporal data
clustering for human motion segmentation (HMS). These approaches leverage prior
knowledge from a source domain to improve clustering performance on a target
domain, and currently they represent the state of the art in HMS. Bucking this
trend, in this paper, we propose a novel unsupervised model that learns a
representation of the data and digs clustering information from the data
itself. Our model is reminiscent of temporal subspace clustering, but presents
two critical differences. First, we learn an auxiliary data matrix that can
deviate from the initial data, hence confer more degrees of freedom to the
coding matrix. Second, we introduce a regularization term for this auxiliary
data matrix that preserves the local geometrical structure present in the
high-dimensional space. The proposed model is efficiently optimized by using an
original Alternating Direction Method of Multipliers (ADMM) formulation
allowing to learn jointly the auxiliary data representation, a nonnegative
dictionary and a coding matrix. Experimental results on four benchmark datasets
for HMS demonstrate that our approach achieves significantly better clustering
performance then state-of-the-art methods, including both unsupervised and more
recent semi-supervised transfer learning approaches.
