Collaborative Low-Rank Subspace Clustering
This work addresses the challenge of integrating multiple data sources for better clustering in machine learning, but appears incremental as it builds on existing subspace and collaborative learning methods.
The paper tackles the problem of learning a unified representation from multiple observations to improve discriminative power in subspace clustering, and shows that their method outperforms separate subspace clustering and a state-of-the-art collaborative learning algorithm in experiments.
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.