Computer Science > Computer Vision and Pattern Recognition
[Submitted on 13 Apr 2017]
Title:Collaborative Low-Rank Subspace Clustering
View PDFAbstract: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.
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