arXiv · 1704.03966
Collaborative Low-Rank Subspace Clustering
Abstract
In this paper we present Collaborative Low-Rank Subspace Clustering. Given multiple observations of a phenomenon we learn a unified representation matrix. This unified matrix incorporates the features from all the observations, thus increasing the discriminative power compared with learning the representation matrix on each observation separately. Experimental evaluation shows that our method outperforms subspace clustering on separate observations and the state of the art collaborative learning algorithm.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Stephen Tierney, Yi Guo, Junbin Gao. 2017-04-13. Collaborative Low-Rank Subspace Clustering. https://arxiv.org/abs/1704.03966
Cite the original work for its findings. Save a collection to share your selection of sources.